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Neural-network based boost pressure prediction for two-stage turbocharging system of diesel engine
9th IFAC International Symposium on Advances in Automotive 9th IFAC International Symposium on Advances in Automotive Control Available at www.sciencedirect.com 9th IFAC France, International Advancesonline in Automotive Control Orléans, June Symposium 23-27, 2019 on 9th IFAC International Symposium on Advances in Automotive Control Orléans, France, June 23-27, 2019 Control Orléans, France, June 23-27, 2019 Orléans, France, June 23-27, 2019
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IFAC PapersOnLine 52-5 (2019) 178–184
Neural-network based boost pressure prediction for two-stage turbocharging system Neural-network based boost pressure prediction for two-stage turbocharging system Neural-network based boost pressure prediction for two-stage turbocharging system of diesel enginefor Neural-network based boost pressure prediction of diesel engine two-stage turbocharging system of diesel engine of diesel engine
Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * MengBeijing, Xia*. Hong Li* *Beijing Institute of Technology, China, *Beijing Institute of Technology, Beijing, China, (e-mail: of hy111@ bit.edu.cn). *Beijing Institute Technology, Beijing, China, (e-mail: of hy111@ bit.edu.cn). *Beijing Institute Technology, Beijing, China, (e-mail: hy111@ bit.edu.cn). (e-mail: hy111@ bit.edu.cn). Abstract: A boost pressure prediction research based on neural network is carried out for a diesel engine Abstract: A boost pressure prediction research neural network is carriedusing out for a diesel engine with two-stage adjustable turbocharging system.based First, on based on the co-simulation GT-POWER and Abstract: A boost pressure prediction research based on neural network is carriedusing out for a diesel engine with two-stage adjustable turbocharging system. First, based on the co-simulation GT-POWER and Abstract: A boost pressure prediction research based on neural network is carried out for a bypass diesel engine MATLAB/Simulink model, the dynamic influence ofbased cycleonfuel injection quantity and valve with two-stage adjustable turbocharging system. First, the co-simulation using GT-POWER and MATLAB/Simulink model, the dynamic influence ofbased cycle fuel injection quantity and bypass strong valve with two-stage First, the co-simulation using GT-POWER and opening of the adjustable turbine onturbocharging boost pressuresystem. of diesel engine isonstudied. The boost pressure shows MATLAB/Simulink model, the dynamic influence of cycle fuel injection quantity and shows bypass strong valve opening of the turbine on boost pressure of diesel engine is studied. The boost pressure MATLAB/Simulink model, the dynamic influence of cycle fuel injection quantity and bypass valve nonlinear relationship with the two aforementioned affecting parameters. Second, a nonlinear opening of relationship the turbine on boost of diesel engineaffecting is studied.parameters. The boost pressure strong nonlinear with thepressure two model aforementioned Second, shows nonlinear opening of theneural turbinenetwork on boost pressure of diesel engine iswith studied. Theinput boost pressure shows strong autoregressive prediction (NARXNN) external is designed toaa predict the nonlinear relationship with the two aforementioned affecting parameters. Second, nonlinear autoregressive neural network prediction model (NARXNN) with external inputvalve is designed toa of predict the nonlinear relationship withengine. the two aforementioned affecting parameters. Second, nonlinear boost pressure of the diesel The cycle fuel injection quantity, bypass opening turbine, autoregressive neural prediction model (NARXNN) with external inputvalve is designed to of predict the boost pressure of pressure the network dieselareengine. cycle injection quantity, opening turbine, autoregressive neural network prediction (NARXNN) external inputtoispredict designed predict the and current boost used asThe themodel inputsfuel of the neuralwith network inbypass order thetofuture boost boost pressure of the diesel engine. The cycle fuel injection quantity, bypass valve opening of turbine, and current boost pressure areengine. used prediction asThe the cycle inputs of the neural network inbypass order to predict the future boost boost pressure of the diesel fuel injection quantity, valve opening of turbine, pressure. Then the neural network model is identified based on the mean absolute percentage and current boost pressure are used prediction as the inputs of the network inonorder to predict the future boost pressure. Then the neural network model is neural identified based the mean percentage and boost pressure are usedresults as the show inputs of the neural network in order to predict the future boost errorcurrent (MAPE). The identification that the minimum MAPE (3.52%) isabsolute achieved when the pressure. Then the neural networkresults prediction model is identified based on (3.52%) the meanis absolute percentage error (MAPE). The identification show that the minimum MAPE achieved when the pressure. Then the neural network prediction model is identified based on the mean absolute percentage number of hidden layer nodes is 15. At last, the neural network prediction model isforachieved boost pressure is error (MAPE). The identification results show that the minimum MAPE (3.52%) when the number of nodes is 15. Atprediction last, neural network prediction model boost pressure is error (MAPE). Thelayer identification results showthe that the minimum MAPE (3.52%) isforachieved when verified byhidden simulation. The one-step simulation results of boost pressure show that the number of hidden layer nodes is 15. At last, the neural network prediction model for boost pressure is verified byhidden simulation. The can one-step simulation results of pressure show that the number of layer nodes is accurately 15. Atprediction last,predict the neural network prediction model forofboost pressure is established prediction model the boost pressure, andboost the MAPE the verification verified by prediction simulation. The can one-step prediction simulation results of boost pressure show that the established model accurately predict the boost pressure, and the MAPE of the verification verified by simulation. The one-step prediction simulation results of the boost pressure show that the data is 3.98%. And the multistep prediction simulation results show that pressure prediction error in established prediction can accurately predict the boost pressure, andthethepressure MAPE prediction of the verification data is 3.98%. And themodel multistep prediction simulation results show that that error in established prediction model canisaccurately predict the boost pressure, andthetheprediction MAPE ofmodel the verification the 50-step prediction domain within 0.05bar, which indicates has high data is 3.98%. And the multistep prediction simulation results show that the pressure prediction error in the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high data is 3.98%. And the multistep prediction simulation results show that the pressure prediction error in accuracy. the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high accuracy. the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high accuracy. © 2019, IFAC (International Federation of Automatic Control) by multi-step Elsevier Ltd. All rights reserved. Keywords: two-stage adjustable turbocharging system; neuralHosting network; prediction accuracy. Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction (see (Buratti Riccardo et al. 1997)) designed a model-based 1. INTRODUCTION (see (BurattiVGT Riccardo et al.considering 1997)) designed a model-based dual-mode controller the effects of engine 1. INTRODUCTION (see (BurattiVGT Riccardo et al.considering 1997)) designed a model-based dual-mode controller the effects of control engine 1. INTRODUCTION (see (Buratti Riccardo et al. 1997)) designed a model-based speed and fuel on boost pressure, and achieved good The turbocharging is1.an important technology to improve the speed dual-mode VGToncontroller considering the effects of control engine INTRODUCTION and fuel boost pressure, and achieved good The turbocharging is an important technology to improve the dual-mode VGT controller considering the effects of engine results. air supply for diesel engines in the plateau. The traditional speed and fuel on boost pressure, and achieved good control The turbocharging is an important technology to improve the results.and fuel on boost pressure, and achieved good control air for diesel engines in the plateau. The traditional Thesupply turbocharging is an important technology to improve the speed turbocharging is limited the pressure ratio and results. air supply for technology diesel engines in theby plateau. The traditional The effective and accurate modelling is the foundation of turbocharging technology is limited by the pressure ratio and results. air supply diesel enginestoinmeet the the plateau. The traditional flow range,for andtechnology it is difficult requirements at high The effective control. and accurate the the foundation of turbocharging is limited by the pressure ratio and model-based The modelling modellingis of two-stage flow range, and it is difficult to meet the requirements at high The effective control. and accurate modelling is of the the foundation of turbocharging technology is limited by the pressure ratiostage and model-based altitudes. Through the control of the high-pressure The modelling two-stage flow range,Through and it is the difficult to meet the requirements at stage high turbocharging The effective and accurate modelling is thecontrol foundation of system for boost pressure is very altitudes. control of the high-pressure model-based control. The modelling of the two-stage flow range, and itvalve, is difficult meet the requirements at high turbocharging system for boost pressure control is very turbine bypass the to two-stage adjustable boosting altitudes. Through the control of the high-pressure stage model-based control. The modelling of the two-stage complicated since there are a large number of parameter to be turbine bypass valve, the two-stage boosting system for pressureofcontrol is to very altitudes. Through the control ofboost the adjustable high-pressure stage turbochargingsince system can continuously adjust the pressure. Therefore, there areisaboost largenonlinear number parameter be turbine can bypass valve, the two-stage adjustable boosting complicated turbocharging system for boost pressure (see control is W.R. very calibrated and the system very (Cook system continuously adjust the boost pressure. Therefore, complicated since there are a large number of parameter to be turbine bypass valve, the two-stage adjustable boosting it is very suitable for solving the problem of variable altitude calibrated and the system is very nonlinear (see (Cook W.R. system can continuously adjust the boost pressure. Therefore, complicated since thereneural are a networks large number parameter to be et al. 2012). Artificial haveofbeen widelyW.R. used it is very suitable for solving the problem of variable altitude calibrated and the system is very nonlinear (see (Cook system can continuously adjust the boost pressure. Therefore, operation of the diesel engines.the problem of variable altitude et al. 2012). Artificial neural networks have been widely used it is very suitable for solving calibrated and the system is very nonlinear (see (Cook W.R. in engine dynamic parameter identification and modelling operation of the diesel engines. et al. 2012).dynamic Artificialparameter neural networks have been used it is very suitable for solving the problem of variable altitude in engine identification andwidely modelling operation of the engines. used turbocharging pressure et al. 2012). Artificial neuralinnetworks have been widely used (see (Moulin P. et al.parameter 2011) recent years. Model predictive At present, thediesel commonly in engine dynamic identification and modelling operation of the diesel engines. (Moulin P.onet neural al.parameter 2011) in recent years. Model predictive At present, the can commonly usedinto turbocharging pressure (see in engine dynamic identification andproblem modelling control based network can solve the of control strategies be divided two main categories: (see (Moulin P.onet neural al. 2011) in recent predictive At present, the can commonly usedinto turbocharging pressure control based canyears. solveModel the problem of control strategies beclosed divided two main categories: (see (Moulin P.oncontrol et neural al. 2011) in recent years. Model predictive modelling and ofnetwork nonlinear systems well. Yao.W. At present, the commonly used turbocharging pressure open loop control and loop control. The open loop control based network can solve the problem of controlloop strategies can beclosed divided into two main modelling andoncontrol nonlinear systems well. Yao.W. open controlto and loop control. The categories: open loop control based network solve the problem of (see (Yao.W.et al.neural 2008)of used thecan neural network model to control isstrategies can be divided into two main categories: simpler implement, and the controller resource is modelling and control of nonlinear systems well. Yao.W. open loop controltoand closed loop control. The resource open loop (see (Yao.W.et al. 2008) used the neural network model to control is simpler implement, and the controller is modelling and control of nonlinear systems well. Yao.W. predict the dynamic response process of the turboshaft engine, open occupied loop control closed loop control. open loop less due and to less calculation tasks.The However, the (see (Yao.W.et al. 2008) used the neural network model to control is simpler implement, and the controller resourcethe is predict the response process oftotheoptimize turboshaft engine, less occupied dueto less calculation tasks. valve However, (Yao.W.et al. 2008) used the neural network model to and use thedynamic model predictive control the cycle control isthe simpler to to implement, andthe thebypass controller resource is (see delay of intake response after opening predict the dynamic response process of the turboshaft engine, less occupied due to less calculation tasks. valve However, the and use thedynamic model predictive control totheoptimize theengine, cycle delay of bypass opening the intake response after the predict the response process of turboshaft fuel injection quantity online which can suppress the less occupied due to less calculation tasks. However, the results in the a longer adjustment process for the boost pressure, and use the model predictive control to optimize the cycle delay ofin intakeadjustment response after the for bypass valve pressure, opening fuel injection quantity online which canturbine, suppress the results a longer process the boost and use the predictive control to optimize thethus cycle andmodel the speed drop of the power to delay of the intakeadjustment response after the bypass valve opening overshoot which isinnot conducive to improve thefor transient performance fuel injection quantity online which can suppress the results a longer process the boost pressure, overshoot and the speed drop of the power turbine, thus to which is not conducive to improve the transient performance fuel injection quantity speed onlineof which can Colin suppress the accelerate the response the system. G (see results in engines. a longer For adjustment process for the boost pressure, of diesel closed loop control, PID control is the overshoot and the speedspeed drop of power turbine, thus to which is not conducive to improve the transient performance the of the the system. Colin G (see of diesel engines. For closed loop PID control is the accelerate overshoot the speed drop of the power turbine, thus to (Colin G etand al. response 2007) established neural network prediction which is not conducive to improve the performance most commonly used method. (seecontrol, (A.transient Chasse et al. 2008)) accelerate the response speed ofaa the system. Colin G (see of diesel engines. For closed loop control, PID control is the (Colin G et al. 2007) established neural network prediction most commonly used method. Chasse et al. 2008)) (see (A. accelerate the response speed of the system. Colin G (see model using the wastegate opening and intake flowrate as its of diesel engines. For closed PI loopcontroller control, PID control the (Colin designed a feedused forward based on isstate G et al. established a neural network prediction most commonly method. (see (A. Chasse et al. model using the2007) wastegate opening and intake flowrate as its designed aSince feedused forward PI controller based on2008)) state (Colin GThe et al. 2007) established acontrol neural network prediction inputs. nonlinear predictive method is used to most commonly method. (see (A.pressure Chasse etaffected al. 2008)) estimation. the change of boost is by model using the wastegate opening and intake flowrate as its designed aSince feed the forward PI controller based on state inputs. The nonlinear predictive control method isdistribute used to estimation. change of boost pressure is affected by model using the wastegate opening and intake flowrate as its optimize the wastegate opening online which can designed feed forward PI and controller based on valve state inputs. The nonlinear predictive control method is used to cycle fuelaSince injection quantity turbine bypass estimation. the change of boost pressure bypass is affected by optimize the nonlinear wastegate opening canisdistribute cycle fuel injection quantity and valve turbine inputs. The predictive control method used to quantity of exhausted gas toonline realizewhich the boost pressure estimation. Since the change of boost pressure is affected by the opening, which makes the boost pressure control a multiple optimize the of wastegate opening online which can distribute cycle fuel injection quantity and turbine bypass valve the quantity exhausted gas to realize the boost pressure opening, which makes the boost pressure control a multiple optimize the wastegate opening online which can distribute control. The bench test was carried out whose results showed cycle fuel injection quantity and turbine bypass valve input and single makes output the control problem, and the operational the quantity of exhausted gas to realize the results boost pressure opening, boostproblem, pressureand control a multiple control. bench test was carried out whose showed input andwhich single makes output control the operational the quantity of exhausted gas to realize thealgorithm boost pressure the The neural network predictive control could opening, which the boost control a problem. multiple that condition change even makes it problem, a pressure nonlinear control control. The bench test was carried out whose results showed input and single output control and the operational that the neural network predictive control algorithm could condition change even makes it a nonlinear control problem. control. The bench testeffect. was carried out whose results showed achieve good control Alippi C and Shi Yanran (see input and single output control problem, and the operational Model-based control is an effective solution to such nonlinear. that the good neural network predictive algorithm condition change even it a nonlinear control problem. achieve effect. Alippi control C and Shi Yanrancould (see Model-based control is makes an effective solution to such nonlinear. the neuralcontrol network predictive control algorithm could condition change even makes it a nonlinear control problem. that good control effect. Alippi C and Shi Yanran (see Model-based control is an effective solution to such nonlinear. achieve achieve good control effect. Alippi C and Shi Yanran (see Model-based control an effectiveFederation solution to nonlinear. 2405-8963 © 2019, IFACis(International of such Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Copyright © 2019 IFAC 178 Peer review©under of International Federation of Automatic Copyright 2019 responsibility IFAC 178Control. 10.1016/j.ifacol.2019.09.029 Copyright © 2019 IFAC 178 Copyright © 2019 IFAC 178
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(Alippi C et al. 2003) & (Shi Yanran et al. 2014)) established BP and RBF air-fuel ratio neural network prediction models, and used Newton iteration method to optimize cycle fuel injection quantity in real time. Compared with the PID control algorithm, it improved the response and reduced the air-fuel ratio overshoot under different working conditions. The basis of neural network predictive control is to achieve accurate multistep prediction of the controlled variable. For the two-stage turbocharging system, neural network can fully reflect the influence of multi-parameters on boost pressure due to its good nonlinear approximation ability.
179
Rated power Max. torque
kW N·m
330 1970
Intercooler Intake manifold Rotation direction of turbocharger
Exhaust manifold
Bypass value
High stage turbocharger
Intake flow Exhaust flow Exhaust Port
In this paper, a neural network boost pressure prediction model is studied for an in-line six-cylinder diesel engine equipped with an adjustable two-stage turbocharger. The boost pressure of the two-stage turbocharging system is affected by multiple parameters, such as the cycle fuel injection quantity and turbine bypass valve opening, with strong nonlinear relationship between the inputs and output. So the dynamic neural network prediction model of the boost pressure is designed for the two-stage turbocharging system covering all working conditions, and the influence of the number of different hidden layer nodes on the pressureprediction model accuracy of the turbocharging system is compared. When the number of hidden layer nodes is 15, MAPE can reach 3.52%. Then the verification simulation of the neural network prediction model is carried out, where the MAPE of the verification data is 3.98%. At last, the multistep prediction of boost pressure is simulated and analysed. The prediction error in 50-step prediction time domain is within 0.05 bar, indicating that the established neural network boost pressure prediction model has high multistep prediction accuracy.
Low stage turbocharger
Air intake
Fig.1. Two-stage turbocharging system with bypass valve 3. ANALYSIS OF FACTORS AFFECTING BOOST PRESSURE IN DYNAMIC WORKING CONDITIONS The boost pressure is directly determined by the two-stage turbocharging system and the ambient pressure. The total pressure ratio of the two-stage turbocharging system is affected by the exhaust energy and the turbine bypass valve opening, and the exhaust energy is affected by the cycle fuel injection quantity and engine speed. Therefore, it can be concluded that the boost pressure is related to the turbine bypass valve opening, the cycle fuel injection quantity, the engine speed, and the environmental pressure. Since the actual engine speed is often controlled through the adjustment of the cycle fuel injection quantity, this paper focuses on the influence of the turbine bypass valve opening and the cycle fuel injection quantity on the boost pressure. The simulation research on its influence law is carried out based on the co-simulation platform which is shown in Fig.2. The co-simulation platform includes six-cylinder diesel engine model in GT-POWER and the control model in Simulink (see (Xia Meng et al. 2016)). The MATLAB/Simulink model is mainly used to control the high-pressure turbine bypass valve opening, the engine speed or torque, and the dynamometer; the GT-POWER model is mainly used to simulate the diesel engine dynamic operational condition. Under different simulation conditions, the cycle fuel injection quantity is calculated by the engine controller using the engine target speed or target toque based on the full-range or two-stage speed control strategy in the controller.
2. TWO-STAGE TURBOCHARGEING SYSTEM The research object of this paper is an in-line six-cylinder electronically controlled diesel engine with a two-stage adjustable turbocharger. The basic parameters of the diesel engine are shown in Table 1. The two-stage turbocharging system is shown in Fig.1. It consists of two exhaust gas turbochargers with series arrangement. The small turbocharger is used as high-pressure stage to increase the torque and dynamic performance of the engine at low speeds; while the large turbocharger is used as low-pressure stage to guarantee the boost demand of the engine at high speed and high load. A bypass valve is arranged near the high-pressure stage turbocharger in order to adjust the total flow area of the turbocharging system to distribute the exhaust energy between the high stage turbocharger and the low stage turbocharger. By doing this we can achieve continuous adjustment of the boost pressure and then adapt the engine boost pressure under different working conditions.
MATLAB/Simulink Bypass value opening
Boost pressure control strategy
Fuel quantity per cycle
Table 1. Basic parameters of the diesel engine
Target engine speed or target engine torque Engine speed controller
Actual engine speed
Load torque
Dynamometer
Two-stage turbocharged diesel engine model
Fuel quantity per cycle/ Actual engine speed/ Bypass value opening
GT-POWER
Parameter Cylinder bore Connecting rod length Piston stroke stroke Compression ratio
Unit mm mm mm / /
Value 132 262 145 4 15：1
Fig.2 Co-simulation platform 3.1 Effect of bypass valve opening on boost pressure 179
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Using the co-simulation platform of the two-stage turbocharging diesel engine (see (Xia Meng et al. 2016)), the simulation of the dynamic influence of the bypass valve on boost pressure is carried out. The two-stage speed regulation mode is adopted on diesel engine, the throttle opening is kept constant, the diesel engine speed is controlled by the dynamometer to remain unchanged, and the target bypass valve opening in the turbocharging controller is given by an external step signal. In the simulation, the speed and the cycle fuel quantity of the diesel engine are remained to 1300r/min and 227mg, respectively, and the bypass valve opening decreases from 15°to 2°in 0.5s. The main simulation result is shown in Fig.3, where BVO, BP, HPTS represent bypass valve opening, boost pressure, high-stage turbine speed respectively.
result is shown in Fig.4, where BP, T, CFI represents boost pressure, engine torque, cycle fuel injection quantity respectively.
Fig.4 Simulation results of different change rate of the quantity of the fuel
It can be seen from Fig.4 that compared with the change of the cycle fuel quantity, the delay of the boost pressure change is about 150ms; when the change rate of the cycle fuel injection quantity is 70mg/s, the response time of the turbocharged pressure reaches 95% of the target value is about 4.2s. The response time increased by 0.5s and 1.8s, respectively, compared with that when the change rate of cycle fuel injection quantity is 35mg / s and 17.5mg/s. During the process of increasing the torque and remaining the speed unchanged of the diesel engine, increasing the change rate of the cycle fuel injection quantity within the air-fuel ratio limit can make the torque respond faster. Under the static engine speed condition, if increasing the cycle fuel injection quantity, the boost pressure will increase accordingly. Within the air-fuel ratio limitation range, the increase of the change rate of cycle fuel injection quantity can make the boost pressure respond faster. Therefore, for the case where the cycle fuel injection quantity changes fast, the influence of change rate of the cycle fuel injection quantity on the dynamic response of the boost pressure should be fully considered.
Fig.3 Simulation results of step change of the bypass valve It can be seen from Fig. 3 that at the 5th second, the bypass valve opening begins to change, and there is a 0.2s time delay in boost pressure response; at the 10th second, the boost pressure reaches 2.57bar, and the response time is approximately 5 seconds from the beginning to 95% bypass valve opening. The target boost pressure is increased by approximately 0.58 bar. Under step change of the bypass valve opening, the boost pressure shows a nonlinear trend that increases rapidly at first and then slowly. Therefore, in the dynamic modelling process of boost pressure, the influence of the bypass valve opening change on the dynamic response of the boost pressure should be considered.
4. DESIGN OF PREDICTIVE MODEL FOR BOOST PRESSURE BASED ON NEURAL NETWORK
3.2 Effect of cycle fuel injection quantity on boost pressure Using the established co-simulation model of the two-stage turbocharged diesel engine, the simulation of the dynamic influence of the cycle fuel injection quantity change on the boost pressure is carried out. The two-stage speed regulation mode is adopted for the diesel engine, where the engine speed is controlled by the dynamometer to remain unchanged, the target bypass valve opening in the supercharging controller is remained unchanged, and the diesel engine cycle fuel injection quantity is direct controlled by adjusting the throttle. In the simulation, the speed of the diesel engine is remained at 1300r/min, and the process of increasing the cycle fuel quantity is completed within 1s, 2s, and 4s, respectively. The corresponding rate change of the cycle fuel injection was 70 mg/s, 35 mg/s and 17.5 mg/s, respectively. The simulation 180
4.1 NARXNN model Nonlinear Autoregressive models with exogenous input neural network (NARXNN) which its output is fed back to the input of the feed forward neural network as part of the NARXNN architecture which led more accurate training phase, was used in output prediction for nonlinear system frequently. The output of the NARXNN is represented using the equation (1). Where u(t) and y(t) can multidimensional represent the input and output of the network respectively, W is a matrix of weights, and f is a nonlinear function. n and m represent the delay of the input and output, respectively.
y(t ) f (u(t ), u(t n), y(t 1), y(t m),W )
(1)
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The architecture of the NARXNN can be in parallel or seriesparallel as shown in Fig.5, where u(t) and y(t) are the actual ˆ( input and the output of the system respectively, y t ) is the
Hidden layer
prediction output of the system. TDL represents the time delayed layer. When the system has multi inputs, different time delay layers can be used. In the parallel architecture, the output of the NARXNN is fed back to the input of the neural network. This has some advantages, for example, the input to the feed forward network is more accurate, which can led to more accurate prediction. Shebani, A.et al. 2018. u (t )
T D L T D L
u (t ) Feed Forward Network
Parallel Architecture
yˆ(t )
y (t )
T D L T D L
Feed Forward Network
181
m(t)
0:3
W
(t )
0:3
W
P(t )
1:2
W
W
k
+
+
Pˆ (t )
15
Fig.6. The boost pressure neural network prediction model
yˆ(t )
4.3 Data acquisition for neural network prediction model identification and verification
Series-Parallel Architecture
The identification and verification data in this paper are obtained by co-simulation of the GT-POWER diesel engine control model and the MATLAB/Simulink diesel engine control model, which are validated by experimental data. Xia.M described the detailed modeling methods and calibration (see (Xia Meng et al. 2016)). The data acquisition process is that engine target speed and load torque are used as inputs to the co-simulation model, and the engine controller is in the speed control mode, under which the PID control strategy is adopted, so the cycle fuel injection quantity is calculated by the engine speed control strategy. In order to cover the full dynamic condition of the engine, the turbine bypass valve opening is given by a pseudo-random signal, which satisfy the constraint at different engine speed. It can be seen from Fig.2 that the actual engine speed and boost pressure can be obtained by the GT-POWER model. So the cycle fuel injection quantity, turbine bypass valve opening and boost pressure can be obtained as the identification and verification data for the neural network prediction model.
Fig.5. Parallel and series-parallel architecture of NARXNN 4.2 Boost pressure prediction model based on neural network According to the simulation results of the third part, the boost pressure has a strong nonlinear relationship with the bypass valve and cycle fuel injection quantity, so the establishment of the boost pressure prediction model should consider the influence of these parameters comprehensively. Since there is delay in the bypass valve actuator and the boost pressure response, the bypass valve opening control signal should be correspondingly delayed in the neural network prediction model. There is also a certain delay between the cycle fuel injection quantity and the boost pressure response, so the control signal of the fuel quantity also should be delayed in the neural network prediction model. Considering that the dynamic process of boost pressure reflects the influence of the cycle fuel injection quantity, engine speed and turbine bypass valve opening, including the effect of multi parameters, so the boost pressure is delayed as the feedback for the prediction model, the series-parallel NARXNN prediction model is used to reflect the dynamic process of multi-parameter effect, realizing the real-time prediction of boost pressure, the architecture of the prediction model is shown in Fig.6, where m ， , P represent cycle fuel injection quantity, turbine bypass valve opening and boost pressure respectively. And the transfer function of the hidden layer node uses hyperbolic tangent function, and the output activation uses linear function. It can be seen from the Fig.6 that the delay step of the bypass valve opening, cycle fuel injection quantity and boost pressure is 3, 3, 2 respectively. and indicates the threshold of the hidden layer nodes and the output layers respectively.
Due to the strong nonlinearity of the change of boost pressure during the dynamic process of diesel engine, the pseudo-random signal is adopted as the engine running condition. The main characteristic parameters of the pseudorandom signal include maximum amplitude, minimum amplitude, maximum interval time and minimum interval time during signal change process. The pseudo-random signal characteristic parameters of the engine, including target engine speed, load torque and turbine bypass valve opening are shown in Table 2. Co-simulation with simulation duration of 1000s is performed to acquire neural network identification and verification data. The sampling period is 10ms and the identification data is obtained by the first 700s co-simulation which includes 70,000 data points, and the verification data is obtained by the co-simulation of the last 300s which includes 30,000 data points.
k
The input conditions used in the data acquisition for identification and verification of the neural network are shown in Fig.7 where the TES, LT, BVO and CIQ represent engine target speed, load torque, bypass valve opening and cycle fuel injection quantity respectively. Table 2. Characteristic parameters of pseudo-random signal of diesel engine 181
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Parameter
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Max amplitude
Minimum amplitude
Max interval /(s)
Minimum interval /(s)
2100
1100
5
3
1900
50
7
1
20
0
6
1
Target engine speed/（r/min） Load torque /(N·m) Bypass valve opening /(°)
and the predicted boost pressure of the neural network identification data when the number of the hidden layer nodes is 15.
Fig.8 The actual and prediction boost pressure under the hidden layer node number is 15 Fig.7 Input condition for the identification of neural network 5. SIMULATION VERIFICATION OF BOOST PRESSURE NEURAL NETWORK PREDICTION MODEL
4.4 Boost pressure neural network prediction model identification
5.1 Simulation verification of one- step boost pressure prediction based on neural network
As mentioned previously in section 3.2, the delayed step of the cycle fuel injection quantity and turbine bypass valve opening are 3 and the delayed step of the boost pressure delay is 2, so the choice of the number of hidden layer nodes affects the accuracy of the boost pressure prediction, the MAPE was used to calculate the accuracy of the prediction model. MAPE is shown in equation (2), where Ai is the actual output, Pi is the predicted output, i is time period, and N is the number of time periods.
MAPE
1 N Ai Pi *100% N i 1 Ai
The fourth part of this paper mainly establishes the NARXNN series-parallel architecture to realize one-step prediction of boost pressure, meanwhile the identification of neural network prediction model is carried out, and the number of hidden layer nodes is determined to be 15. In this section, the simulation verification of the identified neural network prediction model is carried out, the verification data is described in Section 3.3.1, which is derived from the data obtained by the co-simulation condition during the last 300s, and there are totally 30,000 sets of data points. The verification result is shown in Fig.9, the predicted boost pressure is highly consistent with the actual boost pressure, and the MAPE obtained from the verification data is 3.98%.
(2)
Since the number of input nodes is 8, the number of hidden layer nodes is 11~24 respectively based on experience(see(Shebani, A.et al. 2018)), and the MAPE under different number of hidden layer nodes was calculated and shown in Table 5, where the HLNN represents hidden layer nodes number. Table 3. MAPE under different hidden layer nodes HLNN MAPE (%) HLNN MAPE (%)
11 4.0 18 4.02
12 7.71 19 3.91
13 4.25 20 5.85
14 5.21 21 6.03
15 3.52 22 3.53
16 4.84 23 7.08
17 5.63 24 5.81
It can be seen from Table 3 that the number of hidden layer nodes affects the accuracy of boost pressure prediction. When the number of hidden layer nodes is between 11 and 24, the prediction model can accurately predict the boost pressure, and the MAPE value is below 6.5%. When the number of hidden layer nodes is 15, the MAPE is 3.52%, reaching the minimum value; and when the number of hidden layer nodes is 12, the MAPE is 7.71% , reaching the maximum value, so 15 is adopted as the number of the hidden layer nodes. Fig.8 indicates the comparison between the actual boost pressure
Fig.9 The actual and prediction boost pressure under the hidden layer node number is 15
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5.2 Simulation verification of multi- step boost pressure prediction based on neural network When the prediction model is used for model predictive control, multi-step prediction of boost pressure is needed, and at this time, the input boost pressure of the prediction model can only be predicted value, so it is necessary to use the parallel architecture NARXNN for multistep prediction. The implementation process of multi-step prediction for boost pressure is shown in Fig. 10, since the weight matrix and the threshold matrix of the parallel architecture NARXNN model are the same as those of the series-parallel architecture, and during the process of multi-step prediction of boost pressure, the one-step predicted boost pressure will be used as the input of the prediction model to predict the next boost pressure. In this paper, the verification data is obtained from the cosimulation during the last 300s, a total of 30,000 sets of data points which is described in section 4.3.1 and used for the simulation study of boost pressure multistep prediction. In order to verify the multi-step boost pressure prediction at different prediction times, the multi-step boost pressure prediction is conducted on different nodes of the verification data in this paper and the node number 1500, 10500 and 20005 are adopted to carry out the multistep prediction of boost pressure. m(t 2), m(t) (t 2), (t) P(t 2), P(t 1)
NARXNN Prediction model
Pˆ (t n)
Pˆ (t)
m(t 1),
m(t 1)
(t1), (t1) P(t 1), Pˆ (t)
NARXNN Prediction model
Fig.11 Multistep prediction boost pressure and actual boost pressure
Pˆ (t 1)
Fig.10 Multistep prediction of boost pressure When the cycle fuel injection quantity and bypass valve opening are determined, the error of the multistep prediction of boost pressure increases with the increase of the one-step prediction error, and the accuracy of the boost pressure prediction value is the basis of the realization of model-based boost pressure prediction control algorithm, and the simulation of multistep prediction of boost pressure is studied, and the variation of multistep prediction error of boost pressure is obtained. The simulation results are shown in Fig. 11.
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The comparison of multistep prediction values of the boost pressure at different verification data nodes number with actual values are shown in Fig.11. It can be seen from Fig. 11 that at different verification data nodes, as the number of boost pressure prediction steps increases, the boost pressure prediction error becomes larger. However, during 50 prediction steps, the prediction error is within 0.05bar which shows that the boost pressure prediction model also has high prediction accuracy during the multistep prediction of boost pressure; therefore, it can be used as the prediction model for model predictive control. 6. CONCLUSIONS This paper analyzed the influence of cycle fuel injection quantity and turbine bypass valve opening on the boost pressure based on the co-simulation model of GT-POWER and Matlab/Simulink, and the result indicates strong nonlinear characteristics. Therefore, the boost pressure prediction model based on NARXNN is established. The cycle fuel injection quantity, bypass valve opening and the current boost pressure are used as inputs to predict the future boost pressure. According to MAPE, the influence of the number of different hidden layer nodes on the prediction accuracy is compared. When the number of hidden layer nodes is 15, the MAPE reaches its minimum at 3.52%. Then the verification results reveals that the MAPE is 3.98%,
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which indicates that the established boost pressure prediction model has high one-step prediction accuracy. Finally, the multistep prediction of boost pressure is simulated and analyzed based on the cycle fuel injection quantity and the turbine bypass valve opening results. The 50-step prediction error throughout the prediction period is within 0.05bar, indicating that the established neural network boost pressure prediction model has high multistep prediction accuracy. The establishment of the boost pressure prediction model can be applied to the design of model-based nonlinear predictive controller, which can simplify the calibration procedure of PID controller and improve the boost pressure control accuracy. The future research will focus on the design of boost pressure prediction controller and the determination of future cycle fuel injection. REFERENCES A Chasse, P Moulin, P Gautier. (2008). Double Stage Turbocharger Control Strategies Development. American Journal of Surgery, (1): 636-646. Alippi, C., Russis, C. D., & Piuri, V. (2003). A neuralnetwork based control solution to air-fuel ratio control for automotive fuel-injection systems. IEEE Press. Buratti, R., Carlo, A., Lanfranco, E., & Pisoni, A. (1997). Di diesel engine with variable geometry turbocharger (vgt): a model based boost pressure control strategy. Meccanica, 32(5), 409-421. Cook, W. R., Kepes, F., Joseleau-Petit, D., Macalister, T. J., & Rothfield, L. I. (2012). Dynamic fault detection and isolation for automotive engine air path by independent neural network model. International Journal of Engine Research, 15(1), 87-100. Colin, G., Chamaillard, Y., Bloch, G., & Corde, G. (2007). Neural control of fast nonlinear systems— application to a turbocharged SI engine with VCT. IEEE Transactions on Neural Networks, 18(4), 1101. Moulin, P., & Chauvin, J. (2011). Modeling and control of the air system of a turbocharged gasoline engine. Control Engineering Practice, 19(3), 287-297. Shebani, A., & Iwnicki, S. (2018). Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear, 406-407. Shi Yiran. (2014). Research of Non-linear Model Predictive Control Theory and Method for AFR of SI Engines[D]. Ji Lin University. Xia Meng, Changlu Zhao, Ying Huang, Fujun Zhang. (2016) Research of Turbo-supercharging system based on power recovery of diesel engine at plateau [C]. SAE 2016 Commercial Vehicle Engineering Congress. Yao Wenrong. (2008). Nonlinear Model Predictive Control for Turboshaft Engine. ACTA AERONAUTICA ET ASTRONAUTICA SINICA. 29(4), 776-780.
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Neural-network based boost pressure prediction for two-stage turbocharging system Neural-network based boost pressure prediction for two-stage turbocharging system Neural-network based boost pressure prediction for two-stage turbocharging system of diesel enginefor Neural-network based boost pressure prediction of diesel engine two-stage turbocharging system of diesel engine of diesel engine
Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * Meng Xia*. Hong Li* Long Yang* Ying Huang * MengBeijing, Xia*. Hong Li* *Beijing Institute of Technology, China, *Beijing Institute of Technology, Beijing, China, (e-mail: of hy111@ bit.edu.cn). *Beijing Institute Technology, Beijing, China, (e-mail: of hy111@ bit.edu.cn). *Beijing Institute Technology, Beijing, China, (e-mail: hy111@ bit.edu.cn). (e-mail: hy111@ bit.edu.cn). Abstract: A boost pressure prediction research based on neural network is carried out for a diesel engine Abstract: A boost pressure prediction research neural network is carriedusing out for a diesel engine with two-stage adjustable turbocharging system.based First, on based on the co-simulation GT-POWER and Abstract: A boost pressure prediction research based on neural network is carriedusing out for a diesel engine with two-stage adjustable turbocharging system. First, based on the co-simulation GT-POWER and Abstract: A boost pressure prediction research based on neural network is carried out for a bypass diesel engine MATLAB/Simulink model, the dynamic influence ofbased cycleonfuel injection quantity and valve with two-stage adjustable turbocharging system. First, the co-simulation using GT-POWER and MATLAB/Simulink model, the dynamic influence ofbased cycle fuel injection quantity and bypass strong valve with two-stage First, the co-simulation using GT-POWER and opening of the adjustable turbine onturbocharging boost pressuresystem. of diesel engine isonstudied. The boost pressure shows MATLAB/Simulink model, the dynamic influence of cycle fuel injection quantity and shows bypass strong valve opening of the turbine on boost pressure of diesel engine is studied. The boost pressure MATLAB/Simulink model, the dynamic influence of cycle fuel injection quantity and bypass valve nonlinear relationship with the two aforementioned affecting parameters. Second, a nonlinear opening of relationship the turbine on boost of diesel engineaffecting is studied.parameters. The boost pressure strong nonlinear with thepressure two model aforementioned Second, shows nonlinear opening of theneural turbinenetwork on boost pressure of diesel engine iswith studied. Theinput boost pressure shows strong autoregressive prediction (NARXNN) external is designed toaa predict the nonlinear relationship with the two aforementioned affecting parameters. Second, nonlinear autoregressive neural network prediction model (NARXNN) with external inputvalve is designed toa of predict the nonlinear relationship withengine. the two aforementioned affecting parameters. Second, nonlinear boost pressure of the diesel The cycle fuel injection quantity, bypass opening turbine, autoregressive neural prediction model (NARXNN) with external inputvalve is designed to of predict the boost pressure of pressure the network dieselareengine. cycle injection quantity, opening turbine, autoregressive neural network prediction (NARXNN) external inputtoispredict designed predict the and current boost used asThe themodel inputsfuel of the neuralwith network inbypass order thetofuture boost boost pressure of the diesel engine. The cycle fuel injection quantity, bypass valve opening of turbine, and current boost pressure areengine. used prediction asThe the cycle inputs of the neural network inbypass order to predict the future boost boost pressure of the diesel fuel injection quantity, valve opening of turbine, pressure. Then the neural network model is identified based on the mean absolute percentage and current boost pressure are used prediction as the inputs of the network inonorder to predict the future boost pressure. Then the neural network model is neural identified based the mean percentage and boost pressure are usedresults as the show inputs of the neural network in order to predict the future boost errorcurrent (MAPE). The identification that the minimum MAPE (3.52%) isabsolute achieved when the pressure. Then the neural networkresults prediction model is identified based on (3.52%) the meanis absolute percentage error (MAPE). The identification show that the minimum MAPE achieved when the pressure. Then the neural network prediction model is identified based on the mean absolute percentage number of hidden layer nodes is 15. At last, the neural network prediction model isforachieved boost pressure is error (MAPE). The identification results show that the minimum MAPE (3.52%) when the number of nodes is 15. Atprediction last, neural network prediction model boost pressure is error (MAPE). Thelayer identification results showthe that the minimum MAPE (3.52%) isforachieved when verified byhidden simulation. The one-step simulation results of boost pressure show that the number of hidden layer nodes is 15. At last, the neural network prediction model for boost pressure is verified byhidden simulation. The can one-step simulation results of pressure show that the number of layer nodes is accurately 15. Atprediction last,predict the neural network prediction model forofboost pressure is established prediction model the boost pressure, andboost the MAPE the verification verified by prediction simulation. The can one-step prediction simulation results of boost pressure show that the established model accurately predict the boost pressure, and the MAPE of the verification verified by simulation. The one-step prediction simulation results of the boost pressure show that the data is 3.98%. And the multistep prediction simulation results show that pressure prediction error in established prediction can accurately predict the boost pressure, andthethepressure MAPE prediction of the verification data is 3.98%. And themodel multistep prediction simulation results show that that error in established prediction model canisaccurately predict the boost pressure, andthetheprediction MAPE ofmodel the verification the 50-step prediction domain within 0.05bar, which indicates has high data is 3.98%. And the multistep prediction simulation results show that the pressure prediction error in the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high data is 3.98%. And the multistep prediction simulation results show that the pressure prediction error in accuracy. the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high accuracy. the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high accuracy. © 2019, IFAC (International Federation of Automatic Control) by multi-step Elsevier Ltd. All rights reserved. Keywords: two-stage adjustable turbocharging system; neuralHosting network; prediction accuracy. Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction Keywords: two-stage adjustable turbocharging system; neural network; multi-step prediction (see (Buratti Riccardo et al. 1997)) designed a model-based 1. INTRODUCTION (see (BurattiVGT Riccardo et al.considering 1997)) designed a model-based dual-mode controller the effects of engine 1. INTRODUCTION (see (BurattiVGT Riccardo et al.considering 1997)) designed a model-based dual-mode controller the effects of control engine 1. INTRODUCTION (see (Buratti Riccardo et al. 1997)) designed a model-based speed and fuel on boost pressure, and achieved good The turbocharging is1.an important technology to improve the speed dual-mode VGToncontroller considering the effects of control engine INTRODUCTION and fuel boost pressure, and achieved good The turbocharging is an important technology to improve the dual-mode VGT controller considering the effects of engine results. air supply for diesel engines in the plateau. The traditional speed and fuel on boost pressure, and achieved good control The turbocharging is an important technology to improve the results.and fuel on boost pressure, and achieved good control air for diesel engines in the plateau. The traditional Thesupply turbocharging is an important technology to improve the speed turbocharging is limited the pressure ratio and results. air supply for technology diesel engines in theby plateau. The traditional The effective and accurate modelling is the foundation of turbocharging technology is limited by the pressure ratio and results. air supply diesel enginestoinmeet the the plateau. The traditional flow range,for andtechnology it is difficult requirements at high The effective control. and accurate the the foundation of turbocharging is limited by the pressure ratio and model-based The modelling modellingis of two-stage flow range, and it is difficult to meet the requirements at high The effective control. and accurate modelling is of the the foundation of turbocharging technology is limited by the pressure ratiostage and model-based altitudes. Through the control of the high-pressure The modelling two-stage flow range,Through and it is the difficult to meet the requirements at stage high turbocharging The effective and accurate modelling is thecontrol foundation of system for boost pressure is very altitudes. control of the high-pressure model-based control. The modelling of the two-stage flow range, and itvalve, is difficult meet the requirements at high turbocharging system for boost pressure control is very turbine bypass the to two-stage adjustable boosting altitudes. Through the control of the high-pressure stage model-based control. The modelling of the two-stage complicated since there are a large number of parameter to be turbine bypass valve, the two-stage boosting system for pressureofcontrol is to very altitudes. Through the control ofboost the adjustable high-pressure stage turbochargingsince system can continuously adjust the pressure. Therefore, there areisaboost largenonlinear number parameter be turbine can bypass valve, the two-stage adjustable boosting complicated turbocharging system for boost pressure (see control is W.R. very calibrated and the system very (Cook system continuously adjust the boost pressure. Therefore, complicated since there are a large number of parameter to be turbine bypass valve, the two-stage adjustable boosting it is very suitable for solving the problem of variable altitude calibrated and the system is very nonlinear (see (Cook W.R. system can continuously adjust the boost pressure. Therefore, complicated since thereneural are a networks large number parameter to be et al. 2012). Artificial haveofbeen widelyW.R. used it is very suitable for solving the problem of variable altitude calibrated and the system is very nonlinear (see (Cook system can continuously adjust the boost pressure. Therefore, operation of the diesel engines.the problem of variable altitude et al. 2012). Artificial neural networks have been widely used it is very suitable for solving calibrated and the system is very nonlinear (see (Cook W.R. in engine dynamic parameter identification and modelling operation of the diesel engines. et al. 2012).dynamic Artificialparameter neural networks have been used it is very suitable for solving the problem of variable altitude in engine identification andwidely modelling operation of the engines. used turbocharging pressure et al. 2012). Artificial neuralinnetworks have been widely used (see (Moulin P. et al.parameter 2011) recent years. Model predictive At present, thediesel commonly in engine dynamic identification and modelling operation of the diesel engines. (Moulin P.onet neural al.parameter 2011) in recent years. Model predictive At present, the can commonly usedinto turbocharging pressure (see in engine dynamic identification andproblem modelling control based network can solve the of control strategies be divided two main categories: (see (Moulin P.onet neural al. 2011) in recent predictive At present, the can commonly usedinto turbocharging pressure control based canyears. solveModel the problem of control strategies beclosed divided two main categories: (see (Moulin P.oncontrol et neural al. 2011) in recent years. Model predictive modelling and ofnetwork nonlinear systems well. Yao.W. At present, the commonly used turbocharging pressure open loop control and loop control. The open loop control based network can solve the problem of controlloop strategies can beclosed divided into two main modelling andoncontrol nonlinear systems well. Yao.W. open controlto and loop control. The categories: open loop control based network solve the problem of (see (Yao.W.et al.neural 2008)of used thecan neural network model to control isstrategies can be divided into two main categories: simpler implement, and the controller resource is modelling and control of nonlinear systems well. Yao.W. open loop controltoand closed loop control. The resource open loop (see (Yao.W.et al. 2008) used the neural network model to control is simpler implement, and the controller is modelling and control of nonlinear systems well. Yao.W. predict the dynamic response process of the turboshaft engine, open occupied loop control closed loop control. open loop less due and to less calculation tasks.The However, the (see (Yao.W.et al. 2008) used the neural network model to control is simpler implement, and the controller resourcethe is predict the response process oftotheoptimize turboshaft engine, less occupied dueto less calculation tasks. valve However, (Yao.W.et al. 2008) used the neural network model to and use thedynamic model predictive control the cycle control isthe simpler to to implement, andthe thebypass controller resource is (see delay of intake response after opening predict the dynamic response process of the turboshaft engine, less occupied due to less calculation tasks. valve However, the and use thedynamic model predictive control totheoptimize theengine, cycle delay of bypass opening the intake response after the predict the response process of turboshaft fuel injection quantity online which can suppress the less occupied due to less calculation tasks. However, the results in the a longer adjustment process for the boost pressure, and use the model predictive control to optimize the cycle delay ofin intakeadjustment response after the for bypass valve pressure, opening fuel injection quantity online which canturbine, suppress the results a longer process the boost and use the predictive control to optimize thethus cycle andmodel the speed drop of the power to delay of the intakeadjustment response after the bypass valve opening overshoot which isinnot conducive to improve thefor transient performance fuel injection quantity online which can suppress the results a longer process the boost pressure, overshoot and the speed drop of the power turbine, thus to which is not conducive to improve the transient performance fuel injection quantity speed onlineof which can Colin suppress the accelerate the response the system. G (see results in engines. a longer For adjustment process for the boost pressure, of diesel closed loop control, PID control is the overshoot and the speedspeed drop of power turbine, thus to which is not conducive to improve the transient performance the of the the system. Colin G (see of diesel engines. For closed loop PID control is the accelerate overshoot the speed drop of the power turbine, thus to (Colin G etand al. response 2007) established neural network prediction which is not conducive to improve the performance most commonly used method. (seecontrol, (A.transient Chasse et al. 2008)) accelerate the response speed ofaa the system. Colin G (see of diesel engines. For closed loop control, PID control is the (Colin G et al. 2007) established neural network prediction most commonly used method. Chasse et al. 2008)) (see (A. accelerate the response speed of the system. Colin G (see model using the wastegate opening and intake flowrate as its of diesel engines. For closed PI loopcontroller control, PID control the (Colin designed a feedused forward based on isstate G et al. established a neural network prediction most commonly method. (see (A. Chasse et al. model using the2007) wastegate opening and intake flowrate as its designed aSince feedused forward PI controller based on2008)) state (Colin GThe et al. 2007) established acontrol neural network prediction inputs. nonlinear predictive method is used to most commonly method. (see (A.pressure Chasse etaffected al. 2008)) estimation. the change of boost is by model using the wastegate opening and intake flowrate as its designed aSince feed the forward PI controller based on state inputs. The nonlinear predictive control method isdistribute used to estimation. change of boost pressure is affected by model using the wastegate opening and intake flowrate as its optimize the wastegate opening online which can designed feed forward PI and controller based on valve state inputs. The nonlinear predictive control method is used to cycle fuelaSince injection quantity turbine bypass estimation. the change of boost pressure bypass is affected by optimize the nonlinear wastegate opening canisdistribute cycle fuel injection quantity and valve turbine inputs. The predictive control method used to quantity of exhausted gas toonline realizewhich the boost pressure estimation. Since the change of boost pressure is affected by the opening, which makes the boost pressure control a multiple optimize the of wastegate opening online which can distribute cycle fuel injection quantity and turbine bypass valve the quantity exhausted gas to realize the boost pressure opening, which makes the boost pressure control a multiple optimize the wastegate opening online which can distribute control. The bench test was carried out whose results showed cycle fuel injection quantity and turbine bypass valve input and single makes output the control problem, and the operational the quantity of exhausted gas to realize the results boost pressure opening, boostproblem, pressureand control a multiple control. bench test was carried out whose showed input andwhich single makes output control the operational the quantity of exhausted gas to realize thealgorithm boost pressure the The neural network predictive control could opening, which the boost control a problem. multiple that condition change even makes it problem, a pressure nonlinear control control. The bench test was carried out whose results showed input and single output control and the operational that the neural network predictive control algorithm could condition change even makes it a nonlinear control problem. control. The bench testeffect. was carried out whose results showed achieve good control Alippi C and Shi Yanran (see input and single output control problem, and the operational Model-based control is an effective solution to such nonlinear. that the good neural network predictive algorithm condition change even it a nonlinear control problem. achieve effect. Alippi control C and Shi Yanrancould (see Model-based control is makes an effective solution to such nonlinear. the neuralcontrol network predictive control algorithm could condition change even makes it a nonlinear control problem. that good control effect. Alippi C and Shi Yanran (see Model-based control is an effective solution to such nonlinear. achieve achieve good control effect. Alippi C and Shi Yanran (see Model-based control an effectiveFederation solution to nonlinear. 2405-8963 © 2019, IFACis(International of such Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Copyright © 2019 IFAC 178 Peer review©under of International Federation of Automatic Copyright 2019 responsibility IFAC 178Control. 10.1016/j.ifacol.2019.09.029 Copyright © 2019 IFAC 178 Copyright © 2019 IFAC 178
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(Alippi C et al. 2003) & (Shi Yanran et al. 2014)) established BP and RBF air-fuel ratio neural network prediction models, and used Newton iteration method to optimize cycle fuel injection quantity in real time. Compared with the PID control algorithm, it improved the response and reduced the air-fuel ratio overshoot under different working conditions. The basis of neural network predictive control is to achieve accurate multistep prediction of the controlled variable. For the two-stage turbocharging system, neural network can fully reflect the influence of multi-parameters on boost pressure due to its good nonlinear approximation ability.
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Rated power Max. torque
kW N·m
330 1970
Intercooler Intake manifold Rotation direction of turbocharger
Exhaust manifold
Bypass value
High stage turbocharger
Intake flow Exhaust flow Exhaust Port
In this paper, a neural network boost pressure prediction model is studied for an in-line six-cylinder diesel engine equipped with an adjustable two-stage turbocharger. The boost pressure of the two-stage turbocharging system is affected by multiple parameters, such as the cycle fuel injection quantity and turbine bypass valve opening, with strong nonlinear relationship between the inputs and output. So the dynamic neural network prediction model of the boost pressure is designed for the two-stage turbocharging system covering all working conditions, and the influence of the number of different hidden layer nodes on the pressureprediction model accuracy of the turbocharging system is compared. When the number of hidden layer nodes is 15, MAPE can reach 3.52%. Then the verification simulation of the neural network prediction model is carried out, where the MAPE of the verification data is 3.98%. At last, the multistep prediction of boost pressure is simulated and analysed. The prediction error in 50-step prediction time domain is within 0.05 bar, indicating that the established neural network boost pressure prediction model has high multistep prediction accuracy.
Low stage turbocharger
Air intake
Fig.1. Two-stage turbocharging system with bypass valve 3. ANALYSIS OF FACTORS AFFECTING BOOST PRESSURE IN DYNAMIC WORKING CONDITIONS The boost pressure is directly determined by the two-stage turbocharging system and the ambient pressure. The total pressure ratio of the two-stage turbocharging system is affected by the exhaust energy and the turbine bypass valve opening, and the exhaust energy is affected by the cycle fuel injection quantity and engine speed. Therefore, it can be concluded that the boost pressure is related to the turbine bypass valve opening, the cycle fuel injection quantity, the engine speed, and the environmental pressure. Since the actual engine speed is often controlled through the adjustment of the cycle fuel injection quantity, this paper focuses on the influence of the turbine bypass valve opening and the cycle fuel injection quantity on the boost pressure. The simulation research on its influence law is carried out based on the co-simulation platform which is shown in Fig.2. The co-simulation platform includes six-cylinder diesel engine model in GT-POWER and the control model in Simulink (see (Xia Meng et al. 2016)). The MATLAB/Simulink model is mainly used to control the high-pressure turbine bypass valve opening, the engine speed or torque, and the dynamometer; the GT-POWER model is mainly used to simulate the diesel engine dynamic operational condition. Under different simulation conditions, the cycle fuel injection quantity is calculated by the engine controller using the engine target speed or target toque based on the full-range or two-stage speed control strategy in the controller.
2. TWO-STAGE TURBOCHARGEING SYSTEM The research object of this paper is an in-line six-cylinder electronically controlled diesel engine with a two-stage adjustable turbocharger. The basic parameters of the diesel engine are shown in Table 1. The two-stage turbocharging system is shown in Fig.1. It consists of two exhaust gas turbochargers with series arrangement. The small turbocharger is used as high-pressure stage to increase the torque and dynamic performance of the engine at low speeds; while the large turbocharger is used as low-pressure stage to guarantee the boost demand of the engine at high speed and high load. A bypass valve is arranged near the high-pressure stage turbocharger in order to adjust the total flow area of the turbocharging system to distribute the exhaust energy between the high stage turbocharger and the low stage turbocharger. By doing this we can achieve continuous adjustment of the boost pressure and then adapt the engine boost pressure under different working conditions.
MATLAB/Simulink Bypass value opening
Boost pressure control strategy
Fuel quantity per cycle
Table 1. Basic parameters of the diesel engine
Target engine speed or target engine torque Engine speed controller
Actual engine speed
Load torque
Dynamometer
Two-stage turbocharged diesel engine model
Fuel quantity per cycle/ Actual engine speed/ Bypass value opening
GT-POWER
Parameter Cylinder bore Connecting rod length Piston stroke stroke Compression ratio
Unit mm mm mm / /
Value 132 262 145 4 15：1
Fig.2 Co-simulation platform 3.1 Effect of bypass valve opening on boost pressure 179
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Using the co-simulation platform of the two-stage turbocharging diesel engine (see (Xia Meng et al. 2016)), the simulation of the dynamic influence of the bypass valve on boost pressure is carried out. The two-stage speed regulation mode is adopted on diesel engine, the throttle opening is kept constant, the diesel engine speed is controlled by the dynamometer to remain unchanged, and the target bypass valve opening in the turbocharging controller is given by an external step signal. In the simulation, the speed and the cycle fuel quantity of the diesel engine are remained to 1300r/min and 227mg, respectively, and the bypass valve opening decreases from 15°to 2°in 0.5s. The main simulation result is shown in Fig.3, where BVO, BP, HPTS represent bypass valve opening, boost pressure, high-stage turbine speed respectively.
result is shown in Fig.4, where BP, T, CFI represents boost pressure, engine torque, cycle fuel injection quantity respectively.
Fig.4 Simulation results of different change rate of the quantity of the fuel
It can be seen from Fig.4 that compared with the change of the cycle fuel quantity, the delay of the boost pressure change is about 150ms; when the change rate of the cycle fuel injection quantity is 70mg/s, the response time of the turbocharged pressure reaches 95% of the target value is about 4.2s. The response time increased by 0.5s and 1.8s, respectively, compared with that when the change rate of cycle fuel injection quantity is 35mg / s and 17.5mg/s. During the process of increasing the torque and remaining the speed unchanged of the diesel engine, increasing the change rate of the cycle fuel injection quantity within the air-fuel ratio limit can make the torque respond faster. Under the static engine speed condition, if increasing the cycle fuel injection quantity, the boost pressure will increase accordingly. Within the air-fuel ratio limitation range, the increase of the change rate of cycle fuel injection quantity can make the boost pressure respond faster. Therefore, for the case where the cycle fuel injection quantity changes fast, the influence of change rate of the cycle fuel injection quantity on the dynamic response of the boost pressure should be fully considered.
Fig.3 Simulation results of step change of the bypass valve It can be seen from Fig. 3 that at the 5th second, the bypass valve opening begins to change, and there is a 0.2s time delay in boost pressure response; at the 10th second, the boost pressure reaches 2.57bar, and the response time is approximately 5 seconds from the beginning to 95% bypass valve opening. The target boost pressure is increased by approximately 0.58 bar. Under step change of the bypass valve opening, the boost pressure shows a nonlinear trend that increases rapidly at first and then slowly. Therefore, in the dynamic modelling process of boost pressure, the influence of the bypass valve opening change on the dynamic response of the boost pressure should be considered.
4. DESIGN OF PREDICTIVE MODEL FOR BOOST PRESSURE BASED ON NEURAL NETWORK
3.2 Effect of cycle fuel injection quantity on boost pressure Using the established co-simulation model of the two-stage turbocharged diesel engine, the simulation of the dynamic influence of the cycle fuel injection quantity change on the boost pressure is carried out. The two-stage speed regulation mode is adopted for the diesel engine, where the engine speed is controlled by the dynamometer to remain unchanged, the target bypass valve opening in the supercharging controller is remained unchanged, and the diesel engine cycle fuel injection quantity is direct controlled by adjusting the throttle. In the simulation, the speed of the diesel engine is remained at 1300r/min, and the process of increasing the cycle fuel quantity is completed within 1s, 2s, and 4s, respectively. The corresponding rate change of the cycle fuel injection was 70 mg/s, 35 mg/s and 17.5 mg/s, respectively. The simulation 180
4.1 NARXNN model Nonlinear Autoregressive models with exogenous input neural network (NARXNN) which its output is fed back to the input of the feed forward neural network as part of the NARXNN architecture which led more accurate training phase, was used in output prediction for nonlinear system frequently. The output of the NARXNN is represented using the equation (1). Where u(t) and y(t) can multidimensional represent the input and output of the network respectively, W is a matrix of weights, and f is a nonlinear function. n and m represent the delay of the input and output, respectively.
y(t ) f (u(t ), u(t n), y(t 1), y(t m),W )
(1)
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The architecture of the NARXNN can be in parallel or seriesparallel as shown in Fig.5, where u(t) and y(t) are the actual ˆ( input and the output of the system respectively, y t ) is the
Hidden layer
prediction output of the system. TDL represents the time delayed layer. When the system has multi inputs, different time delay layers can be used. In the parallel architecture, the output of the NARXNN is fed back to the input of the neural network. This has some advantages, for example, the input to the feed forward network is more accurate, which can led to more accurate prediction. Shebani, A.et al. 2018. u (t )
T D L T D L
u (t ) Feed Forward Network
Parallel Architecture
yˆ(t )
y (t )
T D L T D L
Feed Forward Network
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m(t)
0:3
W
(t )
0:3
W
P(t )
1:2
W
W
k
+
+
Pˆ (t )
15
Fig.6. The boost pressure neural network prediction model
yˆ(t )
4.3 Data acquisition for neural network prediction model identification and verification
Series-Parallel Architecture
The identification and verification data in this paper are obtained by co-simulation of the GT-POWER diesel engine control model and the MATLAB/Simulink diesel engine control model, which are validated by experimental data. Xia.M described the detailed modeling methods and calibration (see (Xia Meng et al. 2016)). The data acquisition process is that engine target speed and load torque are used as inputs to the co-simulation model, and the engine controller is in the speed control mode, under which the PID control strategy is adopted, so the cycle fuel injection quantity is calculated by the engine speed control strategy. In order to cover the full dynamic condition of the engine, the turbine bypass valve opening is given by a pseudo-random signal, which satisfy the constraint at different engine speed. It can be seen from Fig.2 that the actual engine speed and boost pressure can be obtained by the GT-POWER model. So the cycle fuel injection quantity, turbine bypass valve opening and boost pressure can be obtained as the identification and verification data for the neural network prediction model.
Fig.5. Parallel and series-parallel architecture of NARXNN 4.2 Boost pressure prediction model based on neural network According to the simulation results of the third part, the boost pressure has a strong nonlinear relationship with the bypass valve and cycle fuel injection quantity, so the establishment of the boost pressure prediction model should consider the influence of these parameters comprehensively. Since there is delay in the bypass valve actuator and the boost pressure response, the bypass valve opening control signal should be correspondingly delayed in the neural network prediction model. There is also a certain delay between the cycle fuel injection quantity and the boost pressure response, so the control signal of the fuel quantity also should be delayed in the neural network prediction model. Considering that the dynamic process of boost pressure reflects the influence of the cycle fuel injection quantity, engine speed and turbine bypass valve opening, including the effect of multi parameters, so the boost pressure is delayed as the feedback for the prediction model, the series-parallel NARXNN prediction model is used to reflect the dynamic process of multi-parameter effect, realizing the real-time prediction of boost pressure, the architecture of the prediction model is shown in Fig.6, where m ， , P represent cycle fuel injection quantity, turbine bypass valve opening and boost pressure respectively. And the transfer function of the hidden layer node uses hyperbolic tangent function, and the output activation uses linear function. It can be seen from the Fig.6 that the delay step of the bypass valve opening, cycle fuel injection quantity and boost pressure is 3, 3, 2 respectively. and indicates the threshold of the hidden layer nodes and the output layers respectively.
Due to the strong nonlinearity of the change of boost pressure during the dynamic process of diesel engine, the pseudo-random signal is adopted as the engine running condition. The main characteristic parameters of the pseudorandom signal include maximum amplitude, minimum amplitude, maximum interval time and minimum interval time during signal change process. The pseudo-random signal characteristic parameters of the engine, including target engine speed, load torque and turbine bypass valve opening are shown in Table 2. Co-simulation with simulation duration of 1000s is performed to acquire neural network identification and verification data. The sampling period is 10ms and the identification data is obtained by the first 700s co-simulation which includes 70,000 data points, and the verification data is obtained by the co-simulation of the last 300s which includes 30,000 data points.
k
The input conditions used in the data acquisition for identification and verification of the neural network are shown in Fig.7 where the TES, LT, BVO and CIQ represent engine target speed, load torque, bypass valve opening and cycle fuel injection quantity respectively. Table 2. Characteristic parameters of pseudo-random signal of diesel engine 181
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Parameter
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Max amplitude
Minimum amplitude
Max interval /(s)
Minimum interval /(s)
2100
1100
5
3
1900
50
7
1
20
0
6
1
Target engine speed/（r/min） Load torque /(N·m) Bypass valve opening /(°)
and the predicted boost pressure of the neural network identification data when the number of the hidden layer nodes is 15.
Fig.8 The actual and prediction boost pressure under the hidden layer node number is 15 Fig.7 Input condition for the identification of neural network 5. SIMULATION VERIFICATION OF BOOST PRESSURE NEURAL NETWORK PREDICTION MODEL
4.4 Boost pressure neural network prediction model identification
5.1 Simulation verification of one- step boost pressure prediction based on neural network
As mentioned previously in section 3.2, the delayed step of the cycle fuel injection quantity and turbine bypass valve opening are 3 and the delayed step of the boost pressure delay is 2, so the choice of the number of hidden layer nodes affects the accuracy of the boost pressure prediction, the MAPE was used to calculate the accuracy of the prediction model. MAPE is shown in equation (2), where Ai is the actual output, Pi is the predicted output, i is time period, and N is the number of time periods.
MAPE
1 N Ai Pi *100% N i 1 Ai
The fourth part of this paper mainly establishes the NARXNN series-parallel architecture to realize one-step prediction of boost pressure, meanwhile the identification of neural network prediction model is carried out, and the number of hidden layer nodes is determined to be 15. In this section, the simulation verification of the identified neural network prediction model is carried out, the verification data is described in Section 3.3.1, which is derived from the data obtained by the co-simulation condition during the last 300s, and there are totally 30,000 sets of data points. The verification result is shown in Fig.9, the predicted boost pressure is highly consistent with the actual boost pressure, and the MAPE obtained from the verification data is 3.98%.
(2)
Since the number of input nodes is 8, the number of hidden layer nodes is 11~24 respectively based on experience(see(Shebani, A.et al. 2018)), and the MAPE under different number of hidden layer nodes was calculated and shown in Table 5, where the HLNN represents hidden layer nodes number. Table 3. MAPE under different hidden layer nodes HLNN MAPE (%) HLNN MAPE (%)
11 4.0 18 4.02
12 7.71 19 3.91
13 4.25 20 5.85
14 5.21 21 6.03
15 3.52 22 3.53
16 4.84 23 7.08
17 5.63 24 5.81
It can be seen from Table 3 that the number of hidden layer nodes affects the accuracy of boost pressure prediction. When the number of hidden layer nodes is between 11 and 24, the prediction model can accurately predict the boost pressure, and the MAPE value is below 6.5%. When the number of hidden layer nodes is 15, the MAPE is 3.52%, reaching the minimum value; and when the number of hidden layer nodes is 12, the MAPE is 7.71% , reaching the maximum value, so 15 is adopted as the number of the hidden layer nodes. Fig.8 indicates the comparison between the actual boost pressure
Fig.9 The actual and prediction boost pressure under the hidden layer node number is 15
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5.2 Simulation verification of multi- step boost pressure prediction based on neural network When the prediction model is used for model predictive control, multi-step prediction of boost pressure is needed, and at this time, the input boost pressure of the prediction model can only be predicted value, so it is necessary to use the parallel architecture NARXNN for multistep prediction. The implementation process of multi-step prediction for boost pressure is shown in Fig. 10, since the weight matrix and the threshold matrix of the parallel architecture NARXNN model are the same as those of the series-parallel architecture, and during the process of multi-step prediction of boost pressure, the one-step predicted boost pressure will be used as the input of the prediction model to predict the next boost pressure. In this paper, the verification data is obtained from the cosimulation during the last 300s, a total of 30,000 sets of data points which is described in section 4.3.1 and used for the simulation study of boost pressure multistep prediction. In order to verify the multi-step boost pressure prediction at different prediction times, the multi-step boost pressure prediction is conducted on different nodes of the verification data in this paper and the node number 1500, 10500 and 20005 are adopted to carry out the multistep prediction of boost pressure. m(t 2), m(t) (t 2), (t) P(t 2), P(t 1)
NARXNN Prediction model
Pˆ (t n)
Pˆ (t)
m(t 1),
m(t 1)
(t1), (t1) P(t 1), Pˆ (t)
NARXNN Prediction model
Fig.11 Multistep prediction boost pressure and actual boost pressure
Pˆ (t 1)
Fig.10 Multistep prediction of boost pressure When the cycle fuel injection quantity and bypass valve opening are determined, the error of the multistep prediction of boost pressure increases with the increase of the one-step prediction error, and the accuracy of the boost pressure prediction value is the basis of the realization of model-based boost pressure prediction control algorithm, and the simulation of multistep prediction of boost pressure is studied, and the variation of multistep prediction error of boost pressure is obtained. The simulation results are shown in Fig. 11.
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The comparison of multistep prediction values of the boost pressure at different verification data nodes number with actual values are shown in Fig.11. It can be seen from Fig. 11 that at different verification data nodes, as the number of boost pressure prediction steps increases, the boost pressure prediction error becomes larger. However, during 50 prediction steps, the prediction error is within 0.05bar which shows that the boost pressure prediction model also has high prediction accuracy during the multistep prediction of boost pressure; therefore, it can be used as the prediction model for model predictive control. 6. CONCLUSIONS This paper analyzed the influence of cycle fuel injection quantity and turbine bypass valve opening on the boost pressure based on the co-simulation model of GT-POWER and Matlab/Simulink, and the result indicates strong nonlinear characteristics. Therefore, the boost pressure prediction model based on NARXNN is established. The cycle fuel injection quantity, bypass valve opening and the current boost pressure are used as inputs to predict the future boost pressure. According to MAPE, the influence of the number of different hidden layer nodes on the prediction accuracy is compared. When the number of hidden layer nodes is 15, the MAPE reaches its minimum at 3.52%. Then the verification results reveals that the MAPE is 3.98%,
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which indicates that the established boost pressure prediction model has high one-step prediction accuracy. Finally, the multistep prediction of boost pressure is simulated and analyzed based on the cycle fuel injection quantity and the turbine bypass valve opening results. The 50-step prediction error throughout the prediction period is within 0.05bar, indicating that the established neural network boost pressure prediction model has high multistep prediction accuracy. The establishment of the boost pressure prediction model can be applied to the design of model-based nonlinear predictive controller, which can simplify the calibration procedure of PID controller and improve the boost pressure control accuracy. The future research will focus on the design of boost pressure prediction controller and the determination of future cycle fuel injection. REFERENCES A Chasse, P Moulin, P Gautier. (2008). Double Stage Turbocharger Control Strategies Development. American Journal of Surgery, (1): 636-646. Alippi, C., Russis, C. D., & Piuri, V. (2003). A neuralnetwork based control solution to air-fuel ratio control for automotive fuel-injection systems. IEEE Press. Buratti, R., Carlo, A., Lanfranco, E., & Pisoni, A. (1997). Di diesel engine with variable geometry turbocharger (vgt): a model based boost pressure control strategy. Meccanica, 32(5), 409-421. Cook, W. R., Kepes, F., Joseleau-Petit, D., Macalister, T. J., & Rothfield, L. I. (2012). Dynamic fault detection and isolation for automotive engine air path by independent neural network model. International Journal of Engine Research, 15(1), 87-100. Colin, G., Chamaillard, Y., Bloch, G., & Corde, G. (2007). Neural control of fast nonlinear systems— application to a turbocharged SI engine with VCT. IEEE Transactions on Neural Networks, 18(4), 1101. Moulin, P., & Chauvin, J. (2011). Modeling and control of the air system of a turbocharged gasoline engine. Control Engineering Practice, 19(3), 287-297. Shebani, A., & Iwnicki, S. (2018). Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear, 406-407. Shi Yiran. (2014). Research of Non-linear Model Predictive Control Theory and Method for AFR of SI Engines[D]. Ji Lin University. Xia Meng, Changlu Zhao, Ying Huang, Fujun Zhang. (2016) Research of Turbo-supercharging system based on power recovery of diesel engine at plateau [C]. SAE 2016 Commercial Vehicle Engineering Congress. Yao Wenrong. (2008). Nonlinear Model Predictive Control for Turboshaft Engine. ACTA AERONAUTICA ET ASTRONAUTICA SINICA. 29(4), 776-780.
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