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Research on Parameters Matching Design Method for Planetary Hybrid Logistics Vehicle
5th IFAC Conference on Engine Powertrain 5th IFACand Conference onControl, Simulation and Modeling Changchun, China, September 2018 and online Available at www.sciencedirect.com 5th IFACand Conference onControl,20-22, Engine Powertrain Simulation Modeling 5th IFACand Conference onControl, Simulation and Modeling Engine Powertrain Changchun, China, September 20-22, 2018 Engine and Powertrain Control,20-22, Simulation Changchun, China, September 2018 and Modeling Changchun, China, September 20-22, 2018
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IFAC PapersOnLine 51-31 (2018) 21–28 Research on Parameters Matching Design Method for Planetary Hybrid Research on Parameters Design Method Logistics Vehicle Research on Parameters Matching Matching Design Method for for Planetary Planetary Hybrid Hybrid Research on Parameters Matching Design Method for Planetary Hybrid Logistics Vehicle Logistics Vehicle Dafeng Song* Fukang Yu* Logistics Vehicle
Xiaohua Zeng* Nannan Wang* Dafeng Song* Yang* FukangZhenwei Yu* Dafeng Song* FukangZhenwei Yu* Yang* Xiaohua Zeng* Nannan Wang* Dafeng Song* Yang* FukangZhenwei Yu* Xiaohua Zeng* Nannan Wang* * State Key Laboratory of Automotive Simulation and Control, Jilin University, Xiaohua Zeng* Nannan Yang* Zhenwei Wang* Changchun.China, (e-mail: [email protected]). Simulation * State Key Laboratory of Automotive and Control, Jilin University, * State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun.China, (e-mail: [email protected]). * State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun.China, (e-mail: [email protected]). Changchun.China, (e-mail: [email protected]). Abstract: This article applies planetary hybrid system to logistics vehicles, and parameter matching of key components forarticle vehicle powerplanetary source. In the matching planetary rows, aand method basedmatching on the optimal applies hybrid system toof logistics vehicles, parameter of key Abstract: This This article applies planetary hybrid system toof logistics vehicles, and parameter matching of key Abstract: solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of components for vehicle power source. In the matching planetary rows, a method based on the optimal Abstract: This article applies planetary hybrid system to logistics vehicles, and parameter matching of key components for vehicle power source. In the matching of planetary rows, a method based on the optimal eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of components for vehicle power source. In the matching of planetary rows, a method based on the optimal solution of the eigenvalues of the transmission system efficiency is the used to optimize theand selection of the engine's external characteristic strategy, and they are solved under limit conditions C-WTVC eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using operating respectively. When energy matching is under performed on the super capacitor, the the engine'sconditions external characteristic strategy, anddynamics, they are solved theare limit conditions eigenvalues. Considering the full use of engine the two motors matched in theand caseC-WTVC of using the engine'sconditions external characteristic strategy, and they areconditions solved under the limit conditions and C-WTVC kinematics segmentation is performed on the operating to analyze the energy variation in each operating respectively. When energy matching is performed on the super capacitor, the the engine's external characteristic strategy, and they are solved under the limit conditions and C-WTVC operating conditions respectively. When energy matching is performed on the super capacitor, the segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy kinematics segmentation is performed on the operating conditions analyze the energy variation in each operating conditions respectively. When energy matching is performed on the super capacitor, the kinematics segmentation is performed on the operating conditions to analyze the energy variation in each to prove the reasonable matching method. segment. Finally, a logistics vehicle model is established in Cruise to simulate theenergy dynamics and economy kinematics segmentation is performed on the operating conditions analyze the variation in each segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy to prove the reasonable matching method. segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter © prove 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. to the reasonable matching method. to prove theParameters reasonable matching method. hybird system;Logistics vehicle;Feature parameter power system optimization; Keywords: matching;Planetary Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter power system optimization; Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter optimization; power system optimization; power system working state of the engine. Wang, X.C.(2014)also worked on 1. INTRODUCTION the parameter matching of a Wang, dual-planet mixed-typeworked passenger working state of the engine. X.C.(2014)also on 1. INTRODUCTION state ofbased the engine. Wang, goals；Zhang, X.C.(2014)alsoG.L.(2010) worked on In recent years, the e-commerce logistics market in our country working vehicle system onofdynamic the parameter matching a Wang, dual-planet mixed-typeworked passenger 1. INTRODUCTION working state of the engine. X.C.(2014)also on the parameter matching of a dual-planet mixed-type passenger 1. INTRODUCTION has grown rapidly, followed bylogistics a largemarket number of logistics matched the parameters ofdynamic the hybrid vehicle basedG.L.(2010) on driving In recent years, the e-commerce in our country the vehicle system based onof goals；Zhang, parameter matching a dual-planet mixed-type passenger In recent years, the e-commerce our country on dynamic goals；Zhang, G.L.(2010) vehicles. Planetary hybrid type isa currently thein hybrid vehicle has grown rapidly, followed bylogistics largemarket number of logistics cycle,butsystem didparameters notbased consider the efficiency. Malikopoulos, matched the thesystem hybrid vehicle based on driving In recent years, the e-commerce logistics market inmost our country vehicle system based onof dynamic goals；Zhang, G.L.(2010) has grown rapidly, followed by a large number of logistics matched the parameters of the hybrid vehicle based on driving vehicle system energy saving potential and A. vehicles. hybrid type the most hybrid A.(2016) introduced a the logical stochastic control algorithm cycle,but didparameters not considerof system efficiency. Malikopoulos, has grownPlanetary rapidly, with followed by isa currently large number of logistics matched the the hybrid vehicle based on driving cycle,but did not consider the system efficiency. Malikopoulos, vehicles. Planetary hybrid type is currently the most hybrid comprehensive Applying this system to logistics vehicle systemadvantages. with saving potential and that generates anconsider approximate optimal solution for algorithm the HEV A. A.(2016) introduced a the logical stochastic control vehicles. Planetary hybrid energy type is currently the most hybrid cycle,but did not system efficiency. Malikopoulos, A.(2016) introduced stochastic control vehicle system witheconomic energy potential and A. vehicles bring good andsaving environmental problem. Zhang,aa logical J. et optimal al.(2018)introduced a logical that generates an approximate solution for algorithm the HEV comprehensive advantages. Applying this system to benefits. logistics vehicle will system with energy saving potential and control A. A.(2016) introduced logical stochastic control algorithm comprehensive advantages. Applying this system to logistics that generates an approximate optimal solution for the HEV Due to the complexity of the planetary hybrid system, how to stochastic control algorithm that generates an approximate control problem. Zhang, J. et al.(2018)introduced a logical vehicles will bring good economic and environmental benefits. comprehensive advantages. Applying this system to logistics that generates an approximate optimal solution for the HEV vehicles will bring good economic and environmental benefits. control problem. Zhang, J. et al.(2018)introduced a logical reasonably select the of dynamic system and system, key structural optimalsolution forZhang, the HEV control problem.an approximate Due to the complexity the planetary hybrid how to stochastic control algorithm that generates vehicles will bring good economic and environmental benefits. control problem. J. et al.(2018)introduced a logical Due to the complexity of the planetary hybrid system, how to stochastic control algorithm that generates an approximate parameters in the process of vehicle development to obtain reasonably select the dynamic system and key structural optimalsolution for the HEV control problem. Due to the complexity of the planetary hybrid system, how to stochastic control algorithm thatstudies generates an efficiency approximate In summary, there are not many on the of reasonably select the dynamic system and key structural for the HEV control problem. better dynamics andprocess economy, making theand studykey of parameter parameters in the of vehicle development to obtain optimalsolution reasonably select the dynamic system structural optimalsolution forfeature the HEV control problem. planetary system parameters matching. This paper In summary, there are not many studies on the efficiency parameters in the process of vehicle development to obtain matching appear particularly in study the early of In summary, there are not many studies on the efficiency of better dynamics and economy, makingdevelopment the of parameter parameters in the process of important vehicle tostage obtain of a system method to solve thematching. planetary row feature planetary This paper better dynamics and economy, making the study of parameter In summary, there feature areoptimally not parameters many studies on the efficiency of development. matching appearand particularly in study the early stage of proposes better dynamics economy,important making the of parameter planetary system feature parameters matching. This paper parameters based on transmission efficiency thefeature whole a system method to the optimally solve the planetaryofThis row matching appear particularly important in the early stage of proposes planetary feature parameters matching. paper development. matching appearand particularly important in thehybrid early vehicles, stage of proposes a method to the optimally solvesimulation. the planetaryofrow In the matching optimization of planetary drive cycle, and validates it through parameters based on transmission efficiency thefeature whole development. proposes a method to optimally solve the planetary row feature development. parameters based on the transmission efficiency of the whole scholars in related fields have conducted different degrees of In the matching and optimization of planetary hybrid vehicles, drive cycle, and validates it through simulation. parameters based on power the transmission efficiency of the whole Logistics vehicle In the matching andfields optimization of planetary hybriddegrees vehicles, drive cycle, and validates it system throughconfiguration simulation. research. Nie, L.W. (2012)analyzes the characteristic rotation scholars in related have conducted different of 1.1 In the matching andfields optimization of planetary hybriddegrees vehicles, drive cycle, and validates it system throughconfiguration simulation. 1.1 Logistics vehicle power scholars in related have conducted different of plane thehave geometry selects research. Nie, L.W.to (2012)analyzes theprinciple characteristic rotation TheLogistics selected vehicle planetary hybrid hybrid system configuration is scholarsaccording in related fields conducted differentand degrees of 1.1 power system configuration research. Nie, L.W. (2012)analyzes theprinciple characteristic rotation 1.1 power system configuration according to the rotation speed change, but does not consider plane according to the geometry and selects shown in Fig.vehicle 1. TheLogistics selected planetary hybrid hybrid system configuration is research. Nie, L.W. (2012)analyzes the characteristic rotation plane according to planetary the geometry and selects The selected hybrid hybrid system configuration is the efficiency of rotation the system;Wu, G.Q. not et al.(2009) according to the change,principle but does consider in Fig.planetary 1. plane according to thespeed geometry principle and selects shown The selected planetary hybrid hybrid system configuration is according to the speed change, butgears does not consider shown in Fig. 1. performed dynamic onsystem;Wu, planetary analyzed the efficiency of rotation theanalysis planetary G.Q.and et al.(2009) according to the rotation speed change, but does not consider shown in Fig. 1. the theanalysis planetary system;Wu, G.Q.and et al.(2009) the efficiency layout dynamic of of also gears did consider performed onThey planetary analyzed the efficiency ofthe the structure. planetary system;Wu, G.Q.not et al.(2009) performed dynamic analysis on planetary gears and analyzed efficiency issues in depth. Peng, Z.Y. et al.(2012) also the layout of the structure. They also did not consider performed analysis onThey planetary and the layout dynamic of the structure. alsoofgears did not analyzed consider considered only satisfying thePeng, limitation theal.(2012) power source efficiency issues instructure. depth. Z.Y. et also the layout of the They also did not consider efficiency issues depth. Z.Y.ofin etthe al.(2012) also speed and failed toin study efficiency issues depth.Fan, J.W. considered only satisfying thePeng, limitation power source efficiency issues in depth. Peng, Z.Y. et al.(2012) also considered only satisfying the limitation of the power source et al. and onlyfailed selected the dynamic relationship to select the speed to study efficiency issuesofinthe depth.Fan, J.W. considered only satisfying the limitation power source speed and failed to study efficiency issues in depth.Fan, J.W. planetary gear characteristic parameters.Chen, Q.Q.(2016) et al. only selected the dynamic relationship to select the speed and failed to study efficiency issues in depth.Fan, J.W. et al. the onlygear selected the software dynamic relationship toQ.Q.(2016) select the uses optimization Isight to optimize the planetary characteristic parameters.Chen, et al. only selected the dynamic relationship to select the planetary gear characteristic parameters.Chen, Q.Q.(2016) eigenvalues of the planetary row, taking into account the uses the optimization software Isight to optimize planetary gear characteristic parameters.Chen, Q.Q.(2016) uses the optimization software Isight to the efficiency the system, but it isrow, optimized the condition eigenvalues of the planetary takingunder intooptimize account the uses the ofoptimization software Isight to optimize eigenvalues of the planetary row, taking into account the of the power source；Yu, et optimized al.(2009)introduced a design efficiency of the system, butY. it is condition eigenvalues of the planetary takingunder into the account the efficiency of the system, but it isrow, optimized under the condition methodology forsystem, determining the major system of of the power source；Yu, et optimized al.(2009)introduced a design efficiency of the butY. it is underparameters the condition Fig. 1.Hybrid system configuration the power source；Yu, Y. the etdrive al.(2009)introduced a design aof power-split hybrid vehicle train and optimized the methodology for determining major system parameters of of the power source；Yu, Y. et al.(2009)introduced a design Fig. 1.Hybrid system configuration methodology determining major parametersthe of Fig. 1.Hybrid system configuration amethodology power-split for hybrid vehicle the drive trainsystem and optimized determining major parametersthe of Fig. 1.Hybrid system configuration a power-split for hybrid vehicle the drive trainsystem and optimized Copyright © 2018, 2018 IFAC a power-split hybrid vehicle driveFederation train and optimizedControl) the 21 Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International of Automatic Peer review©under of International Federation of Automatic Copyright 2018 responsibility IFAC 21 Control. Copyright © 2018 IFAC 21 10.1016/j.ifacol.2018.10.005 Copyright © 2018 IFAC 21
IFAC E-CoSM 2018 22 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
The front planetary row PG1 is the power coupling mechanism of the system, which determines the power split characteristics of the system; the function of the rear planetary row PG2 is to adjust the speed and torque of the motor MG2 in order to optimize the selection of the MG2.
The engine power required to reach the given maximum speed as shown in Fig. 2 is 83.24 kW. Considering the power demand of the engine accessory, the engine power can be set to 90 kW.
1.2 Logistics vehicle parameters and dynamic requirements
The characteristic parameter of planetary row plays an important role in the whole mixing system. In this paper, a method based on the transmission efficiency of the whole working condition to solve the characteristic parameters of planetary rows is proposed.
2.2 Matching of characteristic parameters of planetary row
The basic parameters of the vehicle are shown in Table 1, and the dynamic requirements are shown in Table 2. Table 1.Parameters of vehicle
(1) Characteristics of rear planetary row
Gross Weight
4495Kg
Curb Weight
2560Kg
Drag coefficient
0.5375
Frontal Area
6m�
Rolling resistance coefficient
0.0076+0.000056*V
Vehicle parameters
Final Drive Ratio
The rear planetary row plays a role in decelerating and increasing the torque of the motor MG2. Therefore, the selection of the characteristic parameters of the rear planetary row is mainly restricted by the maximum speed and the maximum torque of the motor MG2(Fan, J.W. et al, 2013). According to the requirement of the maximum speed of 110 km/h, the maximum speed of the motor MG2 can be calculated according to equation (2).
4.785
wheel
Rolling radius
𝜔𝜔��� =
0.376m
Dynamic performance requirements
110km/h
Maximum grade
20%@15km/h
acceleration time
0-90km/h<66 s
Overtaking acceleration
60-70km/h<10 s
�.�
∙
����� ��
∙ 30 ∙ 𝑖𝑖�� (1 + 𝑘𝑘� )
(2)
𝑅𝑅���� is the wheel radius; 𝑖𝑖�� is the main reducer gear ratio.
Table 2. The dynamic requirements Maximum speed
����
60-90 km/h<39 s
2. PARAMETERS MATCHING OF POWER SYSTEM 2.1 Engine matching The power storage device in this system uses a super capacitor, which has the characteristics of high power density and small energy density. Therefore, the engine is required to provide all the energy requirements under the steady state conditions to ensure the power capacity of the system. Based on the consideration of dynamics, steady-state conditions mainly include maximum speed cruising and maximum continuous climbing(Li,Y.2016). The engine demand power is calculated based on the maximum speed requirement. �
�
� 𝑃𝑃� = ����� �𝑚𝑚𝑚𝑚𝑚𝑚� + � 𝜌𝜌𝜌𝜌� 𝐴𝐴𝐴𝐴�� + 𝑚𝑚𝑚𝑚𝑚𝑚� �
Fig. 3. New hybrid system configuration Consider the assembly relationship of planetary rows, 𝑘𝑘� > 1.5(Han, L.J. et al, 2014). It is calculated that even when 𝑘𝑘� takes the minimum value of 1.5, the maximum rotation speed of the motor MG2 will exceed 9000 rpm, which is disadvantageous to the selection of the motor. Therefore, consider removing the rear planetary gear mechanism at the beginning of the parameter matching,as shown in Figure 3 After completing the matching and selection of the motor MG2, consider whether to add the rear planetary gear row according to the specific requirements. In this way, the maximum speed of motor MG2 should be greater than 3713 rpm.
(1)
𝑃𝑃� is the engine output power (kw); 𝑉𝑉� is the vehicle speed (m/s); 𝜂𝜂� is the transmission efficiency; 𝑓𝑓� is the rolling resistance coefficient; 𝜌𝜌 is the air density; 𝐶𝐶� is the air resistance coefficient; and A is the vehicle upwind area (m2) ; 𝑖𝑖 is the slope.
(2) Characteristics of the front planetary row The parameters of the front planetary row determine the power split ratio of the system, which in turn affects the transmission efficiency of the system(Wang, X,C. 2014). Next, the relationship between the characteristics of the front planetary row and the transmission efficiency of the system is explored.
Fig. 2.Total Engine Power for Maximum Speed Requirements 22
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Considering the assembly relationship of the planetary gears and the limitation of the motor speed, the 𝑘𝑘� value ranges from [1.5, 3].
Defining the transmission efficiency is the ratio of the output power of the system to the input power. The following relationship can be obtained from the equations of motion and the dynamic equations of the planetary mechanism: ���
���
𝜂𝜂�� =
��
= (1 + 𝑘𝑘� )𝑆𝑆𝑆𝑆 𝑆 𝑆𝑆�
�� ��
�
= (���
Solve the relationship between system efficiency and the characteristic parameters of the front planetary row. Assume that the motor efficiency is a constant value of 0.9. Use the CWTVC operating conditions required by regulations as input. Use the engine's optimal working curve strategy to calculate the comprehensive efficiency of all operating conditions corresponding to different 𝑘𝑘� values. The 𝑘𝑘� value corresponding to the optimal efficiency is finally obtained. The specific solution process is shown in Fig. 4.
(3)
��
(4) (5)
�)
23
𝜔𝜔 represents the speed; subscripts S1 and R1 represent the front planetary row sun gear and ring gear; SR is the transmission ratio of the transmission system, is defined as the input speed ratio output speed (ie engine speed divided by the ring gear speed); 𝑘𝑘� is the former planet row characteristic parameters; 𝑇𝑇 represents torque, subscripts 𝑔𝑔and 𝑒𝑒 represent motor MG1 and engine. The input power of the planetary mechanism is the engine output power on the planet carrier.
𝑃𝑃� = 𝑇𝑇� 𝜔𝜔��
(6)
In the quation, C1 represents the planet carrier of the former planet. The output power consists of mechanical power and electric power.
𝑃𝑃� = 𝑃𝑃��� + 𝑃𝑃���
(7)
Since the mechanical efficiency is relatively high with respect to the motor efficiency, in the analysis, assuming that the mechanical transmission efficiency is 100%, the mechanical power can be expressed as Equation (8).
𝑃𝑃��� =
�� �� ���
(8)
(���� )
Before and after the mechanical point, as the speed or torque direction of the two motors changes, the expression of the electric power also changes. Before the mechanical point, the electrical power can be expressed as:
𝑃𝑃��� = 𝑇𝑇� 𝜔𝜔�� 𝜂𝜂� 𝜂𝜂�
(9)
𝜂𝜂� and 𝜂𝜂� are the efficiencies of the motors MG1 and MG2, respectively. The efficiency of the drive train at the mechanical point can be expressed as:
𝜂𝜂�� =
�� ��
=
�� �� �� ⁄(���� )��� �� �� �� �� ��
Fig. 4. Flowchart for calculating parameters of front planetary row In the figure above, when the required power is less than the minimum power Popt_eng_min on the engine's optimal operating curve, the pure electric mode is used, the system overall efficiency is the electrical system efficiency; when the demand power is greater than the maximum power Popt_eng_max on the engine's optimal working curve, the engine Working at the maximum power point of the optimal working curve, the lack of demand power is supplemented by the motor; in other cases, the engine works on the optimal working curve. All work points in all operating conditions have been determined, and the drive system efficiency and engine efficiency have been calculated based on the system transmission characteristics (Eqs. 9 and 11), and the overall work efficiency of the system has been obtained.
(10)
Equation (8) can be further rewritten as:
𝜂𝜂�� =
�� �[(���� )��� � ]�� �� ��(���� )
(11)
After the mechanical point, the electrical power becomes:
𝑃𝑃��� =
�� ��� �� ��
(12)
The same system drive efficiency expression can be obtained as:
𝜂𝜂�� =
�� �[(���� )����� ]∕�� ∕�� ��(���� )
Perform optimization calculations on different operating conditions and comprehensive conditions of C-WTVC drive
(13)
23
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cycle (4:4:2 according to GB/T 27840-2011, 4495kg feature ratio of urban, roadway, and high-speed parts of trucks). The results are shown in Table.3. Table. 3 Optimized eigenvalues drive cycle
Optimized k1
C-WTVC Urban
3
C-WTVC Roadway
2.26
C-WTVC Freeway
1.5
C-WTVC （4:4:2）
1.64
Fig. 7. 0-90km/h limit acceleration of power variation of each component According to the operating conditions of the motors MG1 and MG2 in the above operating conditions, peak points such as maximum rotation speed, maximum torque, and maximum power can be extracted, as shown in Table 4.
The final parameter of the front planetary row was 1.64. 3. MOTOR MATCHING
Table. 4 Peak acceleration motor operation point 0~90km/h
After the characteristic parameters of the engine and the planetary platoon are determined, based on a reasonable vehicle control strategy, the parameters of the motor can be determined according to the dynamic requirements and operating characteristics of the logistics vehicle. (Zhang, Z.L.et al, 2013). 3.1 Extreme acceleration conditions According to the engine external characteristics strategy, the engine is controlled on the external characteristic curve. After the engine operating point is obtained, the operating points of the motors MG1, MG2 can be calculated separately according to the dynamic relationship of the system. The required speed, torque and power of the motors MG1 and MG2 during the acceleration acceleration process are calculated, as shown in Fig. 5, Fig. 6 and Fig. 7.
Compone nts
Maximum torque
Maximum speed
Maximum power
MG1
140Nm/785r pm
110Nm/2983r pm
-34.4kW/2983rpm
MG2
255.8Nm/25r pm
113Nm/3038 rpm
36.1kW/3038rpm
3.2 Overtaking acceleration conditions The acceleration curve obtained by the above fitting includes acceleration sections of 60-70 km/h and 60-90 km/h, as shown in Fig .8.Among them, the acceleration time of 60-70km/h is less than 10s, and the acceleration time of 60-90km/h is less than 27s, which satisfies the dynamic requirements for overtaking acceleration. Therefore, the power system that satisfies the above extreme acceleration conditions can also meet the overtaking acceleration requirements.
Fig. 5. 0-90km/h limit acceleration power source speed
Fig. 8. Overtaking acceleration curve of 0-90 km/h 3.3 Maximum climbing conditions According to the requirements, the operating points of the motors MG1 and MG2 at a 20% gradient of 15 km/h are calculated, as shown in Figs. 9-11. Fig. 6. 0-90km/h limit acceleration power source torque
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Fig. 9. Maximum speed of power source for climbing conditions
Fig.12.C-WTVC power source speed (MG1 maximum positive speed)
Fig. 10. Maximum power source torque for climbing conditions
Then calculate the MG1 maximum negative speed. Assuming that the engine is off during braking, and because the engine's drag torque is relatively large, it is also assumed that the engine speed drops to zero after the engine is turned off. Therefore, high-speed braking will cause the MG1 to have a large reverse speed, as shown in Fig. 13.Although MG1 does not output torque at this time, the maximum speed exceeds 4600 rpm. Fig. 11. Maximum power requirements for various components in climbing conditions Motor operating points in climbing conditions are shown in Table 5. Table.5 climbing conditions Components
Torque/Nm
Speed/rpm
Power/kW
Engine
300.7
1331
41.9
MG1
-113.9
2683
-32
MG2
606.9
506
32
Super capacitor
--
--
7
Fig.13.C-WTVC power source speed (MG1 maximum negative speed) 3.4.2 Torque requirement In the case of C-WTVC, the required torque of the torque of each power source is shown in Fig. 14, in which the maximum driving torque of MG2 appears in the pure electric mode, which is 427 Nm, and does not exceed the peak performance of the climbing performance for MG2.The maximum braking torque of MG2 is -398Nm. Demand torque of MG1 is a maximum of -134Nm, which is also less than the requirement of climbing performance
3.4 Typical driving conditions C-WTVC In addition to the above power performance requirements, motors MG1, MG2 should also be able to meet the power requirements under typical driving conditions(Tian, B.C.2011). As mentioned earlier, this model should use 4:4:2 C-WTVC operating conditions. 3.4.1 Speed requirement The maximum speed of MG2 is positively related to the vehicle speed. The maximum speed of C-WTVC operating conditions is less than 110km/h, so the maximum speed of MG2 will not exceed 3800rpm. The MG1 speed is affected by both the vehicle speed and the engine speed. First, in the case of low-speed and high-torque driving, due to the high engine speed (the demand power on the optimal working curve is approximately proportional to the speed), the ring gear rotates at a low speed, and the MG1 will have a large forward speed, as shown in Fig. 12. The maximum forward speed of MG1 does not exceed 2500 rpm.
Fig. 14. C-WTVC power source demand torque 3.4.3 Power requirement The power demand of the system and each power source under C-WTVC is shown in Fig. 15. The maximum power of MG1 does not exceed 25 kW, the maximum power of MG2 does not exceed 20 kW, and the maximum power of super capacitors does not exceed 20 kW. The previous match is reasonable.
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charge/discharge efficiency is 0.9, the effective power of the supercapacitor should be greater than 0.23 kWh. 0.1
Battery Energy/kWh
0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25
Fig. 15. Power demand of C-WTVC power source
Hybrid Logistics Vehicle >37 kw
Peak torque
>610 Nm
Base speed
>506rpm
Maximum speed
>3800rpm
peak power
>35 kw
Base speed
>780rpm
Maximum speed
>4600rpm
MG2
MG1
400
600
800 1000 Time/s
1200
1400
1600
1800
Further, statistics are made on each kinematic segment in the working condition under the current electric power usage condition, and the change of the electric energy in each kinematic segment is shown in Fig. 18, and the maximum is 0.12 kWh.Within each kinematics segment, the maximum discharged electric energy of the super capacitor discharge is shown in the figure below. The maximum value is 0.18 kWh, as shown in Fig. 19.
Table.6 Matching Results of Motors MG1 and MG2
peak power
200
Fig. 17.Changes in supercapacitor energy in cycling conditions
Based on the above calculation results, the design requirements for motors MG1 and MG2 are summarized as follows:
Components
0
4. MATCHING SUPERCAPACITORS
Fig. 18. Change in electrical energy per kinematic fragment
The supercapacitor's power and energy should be able to meet the power and energy requirements in the operating conditions and extreme acceleration conditions. 4.1 Energy Matching First, according to the calculation results under the extreme acceleration condition, the supercapacitor energy change in the entire acceleration condition is shown in Fig. 16. At the time of the termination of acceleration, the supercapacitor emits 0.1126kWh of electric energy.
Fig. 19. Super capacitor discharges maximum power for each kinematic fragment In summary, the energy of the super capacitor should be greater than 0.23 kWh. 4.2 Power Matching According to Fig. 16, the maximum required power of the super capacitor in the limit acceleration condition can be found to be 26.64kw.
Fig.16. Changes in supercapacitor energy in extreme acceleration conditions
Analyze the power variation of the super capacitor in the calculation of operating conditions, as shown in Fig. 20.The super capacitor discharge power is within 20kW, and the maximum power is in the case of regenerative braking. According to the maximum power limit of the MG2, the maximum recovery power of the supercapacitor is 37kW.
The second,super capacitor should be able to meet operating conditions, including requiring as much regenerative braking energy as possible during operation.The power usage in the entire operating condition is shown in Fig. 17. It can be seen that the maximum power change of the supercapacitor is 0.21 kWh. In the initial stage, assuming the supercapacitor
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27
Table.7 Simulation of the dynamic performance Project
parameters
Maximum speed
115km/h
0-90km/h acceleration time (s)
32.3
60-70km/h
4.8
acceleration time (s)
Fig. 20. C-WTVC drive cycle super capacitor demand power
60-90km/h
In summary, the power of the super capacitor should be greater than 37 kW.
acceleration time (s)
21.8
Maximum climbing grade
20%@15km/h
5. SIMULATION 5.2 Economic verification
According to the matching parameters, the simulation model is established in Cruise.
The fuel consumptions of the EVT logistics vehicle and the conventional vehicle were simulated separately in the CWTVC city section, highway segment and high-speed segment. The statistics are shown in Table 8. Table.8 EVT Logistics Vehicle Fuel Economy
Drive cycle
CWTVC urban
Fig. 21. Simulation model The simulation results and SOC changes of the speed of the EVT logistics vehicle under the C-WTVC operating conditions are shown in Fig. 22. It can be seen that the matched EVT logistics vehicle has good speed following the C-WTVC operating conditions, and the SOC can maintain balance in the entire working condition and remains at more than 50% to meet the conditions of use.
CWTVC roadway
CWTVC freeway
CWTVC
Fuel Consumptio n (L/100 km)
electricit y consum ption
Comprehe nsive Fuel Consumpt ion (L/100km)
Fuelsaving ration
Traditional vehicle
14
/
14.00
/
EVT
7.94
-1.63
7.40
47.14%
Traditional vehicle
11.94
/
11.94
/
EVT
9.09
1.96
9.74
18.43%
Traditional vehicle
14.42
/
14.42
/
EVT
11.22
-0.12
11.18
22.47%
Traditional vehicle
/
/
13.26
/
EVT
/
/
9.09
31.45%
Vehicle Type
(kWh/1 00km)
The hybrid fuel vehicle obtains a more prominent fuel saving rate under C-WTVC cycle condition, and the comprehensive fuel saving rate is 31.45%.It is proved that the matching optimization method has a remarkable effect. 5.3 Engine work points analysis Fig. 22. C-WTVC speed following and SOC simulation results
We use the engine optimal control strategy,Calculated the optimal operating line(OOL).Through the assistance of the motor, the engine is controlled to the optimal line to achieve the purpose of fuel saving.
5.1 Dynamic verification The dynamic performance of the logistics vehicle obtained by simulation is shown in Table 7. The matching logistics vehicle can meet the design requirements. 27
IFAC E-CoSM 2018 28 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
Torque（Nm）
Nie,L.W.(2012). Parameter Matching and Control of Double Planetary Fluid Powered Hybrid Systems . Jilin University, Changchun,China. Peng, Z.Y, Qin, D.T., Duan, Z.H., Yang, Y.L.(2012) Working model analysis and parameter matching design of new hybrid vehicle. China Mechanical Engineering, 23(09): 1122-1128. Tian, B.C.(2011) Parameter Matching and Simulation of Series Hybrid City Bus Systems. Hunan University, Changsha,China. Wu, G.Q., Qin D.T., Hu J.C., Ye M.(2009).Research on Scheme and Parameter Matching of Hybrid Power Planetary Power Transmission System.Journal of Machine Design, 26(06):60-65.
Speed (r/min)
Fig. 23 Engine work points
Wang, X.C.(2014). Research on double-row planetary gear mechanism for hybrid passenger cars. Southwest Jiaotong University, Chongqing,China.
In Fig. 23, the blue line is the engine's external characteristic curve; the yellow line is the optimal operating line; the red point is the engine's operating point. It can be seen that most of the operating points of the engine are distributed along the OOL line, indicating that the control effect is good.
Yu, Y., Gao, Y., Peng, H., et al.(2009). Parametric design of power-split HEV drive train.Vehicle Power and Propulsion Conference,2009. VPPC'09. IEEE. IEEE, 2009:1058-1063. Zhang, Z.L., et al.(2013). Parameter Matching of Parallel Hybrid Hydraulic System for Urban Passenger Cars. China Journal of Highway and Transport, 26(3): 176-182.
6. CONCLUSION The planetary hybrid system is applied to the logistics vehicle. Based on the optimal solution of the eigenvalues of the transmission system based on the full operating conditions, the front planetary rows are matched to obtain the optimal parameters.According to the extreme working conditions, the primary motors are selected for the dual motors, and then the comprehensive matching of the motor's demand for the motors under typical operating conditions is considered. By dividing the working condition kinematic segments, matched super capacitors.Finally, the simulation model is established in Cruise to verify the dynamics and economy of the whole vehicle, and the rationality of the matching method is proved.
Zhang, G.L.(2010). Research on parameter matching, control strategy and simulation platform for hybrid vehicle based on driving conditions. South China University of Technology, Guangzhou,China. Malikopoulos, A. A. (2016). A multiobjective optimization framework for online stochastic optimal control in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 24(2), 440-450. Zhang, J., & Yuhu, W. U. (2018). A stochastic logical modelbased approximate solution for energy management problem of hevs. Science China(Information Sciences), 61(7), 70207.
ACKNOWLEDGEMENT This study was financially supported by the National Key Research and Development Program (2018YFB 0105900). REFERENCE Chen, Q.Q.(2017).Study on control strategy and parameter optimization of power split dual-mode hybrid bus. Jilin University, Changchun,China. Fan, J.W., Guo,Y.F., Lin,C., Huang,L.M.(2013).Parametric design of dual-motor planetary coupled transmission system for pure electric buses. Science Technology and Engineering, 13(35): 10741-10744+10749. Han, L.J, Liu, H., Wang, W.D, et al.(2014). Research on parameter matching and optimization of power split hybrid vehicle. Automobile Engineering, (8):904-910. Li,Y.,(2016)Parameter matching and optimization of power source for parallel hybrid buses. Beijing Institute of Technology, Beijing,Chian.
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IFAC PapersOnLine 51-31 (2018) 21–28 Research on Parameters Matching Design Method for Planetary Hybrid Research on Parameters Design Method Logistics Vehicle Research on Parameters Matching Matching Design Method for for Planetary Planetary Hybrid Hybrid Research on Parameters Matching Design Method for Planetary Hybrid Logistics Vehicle Logistics Vehicle Dafeng Song* Fukang Yu* Logistics Vehicle
Xiaohua Zeng* Nannan Wang* Dafeng Song* Yang* FukangZhenwei Yu* Dafeng Song* FukangZhenwei Yu* Yang* Xiaohua Zeng* Nannan Wang* Dafeng Song* Yang* FukangZhenwei Yu* Xiaohua Zeng* Nannan Wang* * State Key Laboratory of Automotive Simulation and Control, Jilin University, Xiaohua Zeng* Nannan Yang* Zhenwei Wang* Changchun.China, (e-mail: [email protected]). Simulation * State Key Laboratory of Automotive and Control, Jilin University, * State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun.China, (e-mail: [email protected]). * State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun.China, (e-mail: [email protected]). Changchun.China, (e-mail: [email protected]). Abstract: This article applies planetary hybrid system to logistics vehicles, and parameter matching of key components forarticle vehicle powerplanetary source. In the matching planetary rows, aand method basedmatching on the optimal applies hybrid system toof logistics vehicles, parameter of key Abstract: This This article applies planetary hybrid system toof logistics vehicles, and parameter matching of key Abstract: solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of components for vehicle power source. In the matching planetary rows, a method based on the optimal Abstract: This article applies planetary hybrid system to logistics vehicles, and parameter matching of key components for vehicle power source. In the matching of planetary rows, a method based on the optimal eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of components for vehicle power source. In the matching of planetary rows, a method based on the optimal solution of the eigenvalues of the transmission system efficiency is the used to optimize theand selection of the engine's external characteristic strategy, and they are solved under limit conditions C-WTVC eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using solution of the eigenvalues of the transmission system efficiency is used to optimize the selection of eigenvalues. Considering the full use of engine dynamics, the two motors are matched in the case of using operating respectively. When energy matching is under performed on the super capacitor, the the engine'sconditions external characteristic strategy, anddynamics, they are solved theare limit conditions eigenvalues. Considering the full use of engine the two motors matched in theand caseC-WTVC of using the engine'sconditions external characteristic strategy, and they areconditions solved under the limit conditions and C-WTVC kinematics segmentation is performed on the operating to analyze the energy variation in each operating respectively. When energy matching is performed on the super capacitor, the the engine's external characteristic strategy, and they are solved under the limit conditions and C-WTVC operating conditions respectively. When energy matching is performed on the super capacitor, the segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy kinematics segmentation is performed on the operating conditions analyze the energy variation in each operating conditions respectively. When energy matching is performed on the super capacitor, the kinematics segmentation is performed on the operating conditions to analyze the energy variation in each to prove the reasonable matching method. segment. Finally, a logistics vehicle model is established in Cruise to simulate theenergy dynamics and economy kinematics segmentation is performed on the operating conditions analyze the variation in each segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy to prove the reasonable matching method. segment. Finally, a logistics vehicle model is established in Cruise to simulate the dynamics and economy Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter © prove 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. to the reasonable matching method. to prove theParameters reasonable matching method. hybird system;Logistics vehicle;Feature parameter power system optimization; Keywords: matching;Planetary Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter power system optimization; Keywords: Parameters matching;Planetary hybird system;Logistics vehicle;Feature parameter optimization; power system optimization; power system working state of the engine. Wang, X.C.(2014)also worked on 1. INTRODUCTION the parameter matching of a Wang, dual-planet mixed-typeworked passenger working state of the engine. X.C.(2014)also on 1. INTRODUCTION state ofbased the engine. Wang, goals；Zhang, X.C.(2014)alsoG.L.(2010) worked on In recent years, the e-commerce logistics market in our country working vehicle system onofdynamic the parameter matching a Wang, dual-planet mixed-typeworked passenger 1. INTRODUCTION working state of the engine. X.C.(2014)also on the parameter matching of a dual-planet mixed-type passenger 1. INTRODUCTION has grown rapidly, followed bylogistics a largemarket number of logistics matched the parameters ofdynamic the hybrid vehicle basedG.L.(2010) on driving In recent years, the e-commerce in our country the vehicle system based onof goals；Zhang, parameter matching a dual-planet mixed-type passenger In recent years, the e-commerce our country on dynamic goals；Zhang, G.L.(2010) vehicles. Planetary hybrid type isa currently thein hybrid vehicle has grown rapidly, followed bylogistics largemarket number of logistics cycle,butsystem didparameters notbased consider the efficiency. Malikopoulos, matched the thesystem hybrid vehicle based on driving In recent years, the e-commerce logistics market inmost our country vehicle system based onof dynamic goals；Zhang, G.L.(2010) has grown rapidly, followed by a large number of logistics matched the parameters of the hybrid vehicle based on driving vehicle system energy saving potential and A. vehicles. hybrid type the most hybrid A.(2016) introduced a the logical stochastic control algorithm cycle,but didparameters not considerof system efficiency. Malikopoulos, has grownPlanetary rapidly, with followed by isa currently large number of logistics matched the the hybrid vehicle based on driving cycle,but did not consider the system efficiency. Malikopoulos, vehicles. Planetary hybrid type is currently the most hybrid comprehensive Applying this system to logistics vehicle systemadvantages. with saving potential and that generates anconsider approximate optimal solution for algorithm the HEV A. A.(2016) introduced a the logical stochastic control vehicles. Planetary hybrid energy type is currently the most hybrid cycle,but did not system efficiency. Malikopoulos, A.(2016) introduced stochastic control vehicle system witheconomic energy potential and A. vehicles bring good andsaving environmental problem. Zhang,aa logical J. et optimal al.(2018)introduced a logical that generates an approximate solution for algorithm the HEV comprehensive advantages. Applying this system to benefits. logistics vehicle will system with energy saving potential and control A. A.(2016) introduced logical stochastic control algorithm comprehensive advantages. Applying this system to logistics that generates an approximate optimal solution for the HEV Due to the complexity of the planetary hybrid system, how to stochastic control algorithm that generates an approximate control problem. Zhang, J. et al.(2018)introduced a logical vehicles will bring good economic and environmental benefits. comprehensive advantages. Applying this system to logistics that generates an approximate optimal solution for the HEV vehicles will bring good economic and environmental benefits. control problem. Zhang, J. et al.(2018)introduced a logical reasonably select the of dynamic system and system, key structural optimalsolution forZhang, the HEV control problem.an approximate Due to the complexity the planetary hybrid how to stochastic control algorithm that generates vehicles will bring good economic and environmental benefits. control problem. J. et al.(2018)introduced a logical Due to the complexity of the planetary hybrid system, how to stochastic control algorithm that generates an approximate parameters in the process of vehicle development to obtain reasonably select the dynamic system and key structural optimalsolution for the HEV control problem. Due to the complexity of the planetary hybrid system, how to stochastic control algorithm thatstudies generates an efficiency approximate In summary, there are not many on the of reasonably select the dynamic system and key structural for the HEV control problem. better dynamics andprocess economy, making theand studykey of parameter parameters in the of vehicle development to obtain optimalsolution reasonably select the dynamic system structural optimalsolution forfeature the HEV control problem. planetary system parameters matching. This paper In summary, there are not many studies on the efficiency parameters in the process of vehicle development to obtain matching appear particularly in study the early of In summary, there are not many studies on the efficiency of better dynamics and economy, makingdevelopment the of parameter parameters in the process of important vehicle tostage obtain of a system method to solve thematching. planetary row feature planetary This paper better dynamics and economy, making the study of parameter In summary, there feature areoptimally not parameters many studies on the efficiency of development. matching appearand particularly in study the early stage of proposes better dynamics economy,important making the of parameter planetary system feature parameters matching. This paper parameters based on transmission efficiency thefeature whole a system method to the optimally solve the planetaryofThis row matching appear particularly important in the early stage of proposes planetary feature parameters matching. paper development. matching appearand particularly important in thehybrid early vehicles, stage of proposes a method to the optimally solvesimulation. the planetaryofrow In the matching optimization of planetary drive cycle, and validates it through parameters based on transmission efficiency thefeature whole development. proposes a method to optimally solve the planetary row feature development. parameters based on the transmission efficiency of the whole scholars in related fields have conducted different degrees of In the matching and optimization of planetary hybrid vehicles, drive cycle, and validates it through simulation. parameters based on power the transmission efficiency of the whole Logistics vehicle In the matching andfields optimization of planetary hybriddegrees vehicles, drive cycle, and validates it system throughconfiguration simulation. research. Nie, L.W. (2012)analyzes the characteristic rotation scholars in related have conducted different of 1.1 In the matching andfields optimization of planetary hybriddegrees vehicles, drive cycle, and validates it system throughconfiguration simulation. 1.1 Logistics vehicle power scholars in related have conducted different of plane thehave geometry selects research. Nie, L.W.to (2012)analyzes theprinciple characteristic rotation TheLogistics selected vehicle planetary hybrid hybrid system configuration is scholarsaccording in related fields conducted differentand degrees of 1.1 power system configuration research. Nie, L.W. (2012)analyzes theprinciple characteristic rotation 1.1 power system configuration according to the rotation speed change, but does not consider plane according to the geometry and selects shown in Fig.vehicle 1. TheLogistics selected planetary hybrid hybrid system configuration is research. Nie, L.W. (2012)analyzes the characteristic rotation plane according to planetary the geometry and selects The selected hybrid hybrid system configuration is the efficiency of rotation the system;Wu, G.Q. not et al.(2009) according to the change,principle but does consider in Fig.planetary 1. plane according to thespeed geometry principle and selects shown The selected planetary hybrid hybrid system configuration is according to the speed change, butgears does not consider shown in Fig. 1. performed dynamic onsystem;Wu, planetary analyzed the efficiency of rotation theanalysis planetary G.Q.and et al.(2009) according to the rotation speed change, but does not consider shown in Fig. 1. the theanalysis planetary system;Wu, G.Q.and et al.(2009) the efficiency layout dynamic of of also gears did consider performed onThey planetary analyzed the efficiency ofthe the structure. planetary system;Wu, G.Q.not et al.(2009) performed dynamic analysis on planetary gears and analyzed efficiency issues in depth. Peng, Z.Y. et al.(2012) also the layout of the structure. They also did not consider performed analysis onThey planetary and the layout dynamic of the structure. alsoofgears did not analyzed consider considered only satisfying thePeng, limitation theal.(2012) power source efficiency issues instructure. depth. Z.Y. et also the layout of the They also did not consider efficiency issues depth. Z.Y.ofin etthe al.(2012) also speed and failed toin study efficiency issues depth.Fan, J.W. considered only satisfying thePeng, limitation power source efficiency issues in depth. Peng, Z.Y. et al.(2012) also considered only satisfying the limitation of the power source et al. and onlyfailed selected the dynamic relationship to select the speed to study efficiency issuesofinthe depth.Fan, J.W. considered only satisfying the limitation power source speed and failed to study efficiency issues in depth.Fan, J.W. planetary gear characteristic parameters.Chen, Q.Q.(2016) et al. only selected the dynamic relationship to select the speed and failed to study efficiency issues in depth.Fan, J.W. et al. the onlygear selected the software dynamic relationship toQ.Q.(2016) select the uses optimization Isight to optimize the planetary characteristic parameters.Chen, et al. only selected the dynamic relationship to select the planetary gear characteristic parameters.Chen, Q.Q.(2016) eigenvalues of the planetary row, taking into account the uses the optimization software Isight to optimize planetary gear characteristic parameters.Chen, Q.Q.(2016) uses the optimization software Isight to the efficiency the system, but it isrow, optimized the condition eigenvalues of the planetary takingunder intooptimize account the uses the ofoptimization software Isight to optimize eigenvalues of the planetary row, taking into account the of the power source；Yu, et optimized al.(2009)introduced a design efficiency of the system, butY. it is condition eigenvalues of the planetary takingunder into the account the efficiency of the system, but it isrow, optimized under the condition methodology forsystem, determining the major system of of the power source；Yu, et optimized al.(2009)introduced a design efficiency of the butY. it is underparameters the condition Fig. 1.Hybrid system configuration the power source；Yu, Y. the etdrive al.(2009)introduced a design aof power-split hybrid vehicle train and optimized the methodology for determining major system parameters of of the power source；Yu, Y. et al.(2009)introduced a design Fig. 1.Hybrid system configuration methodology determining major parametersthe of Fig. 1.Hybrid system configuration amethodology power-split for hybrid vehicle the drive trainsystem and optimized determining major parametersthe of Fig. 1.Hybrid system configuration a power-split for hybrid vehicle the drive trainsystem and optimized Copyright © 2018, 2018 IFAC a power-split hybrid vehicle driveFederation train and optimizedControl) the 21 Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International of Automatic Peer review©under of International Federation of Automatic Copyright 2018 responsibility IFAC 21 Control. Copyright © 2018 IFAC 21 10.1016/j.ifacol.2018.10.005 Copyright © 2018 IFAC 21
IFAC E-CoSM 2018 22 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
The front planetary row PG1 is the power coupling mechanism of the system, which determines the power split characteristics of the system; the function of the rear planetary row PG2 is to adjust the speed and torque of the motor MG2 in order to optimize the selection of the MG2.
The engine power required to reach the given maximum speed as shown in Fig. 2 is 83.24 kW. Considering the power demand of the engine accessory, the engine power can be set to 90 kW.
1.2 Logistics vehicle parameters and dynamic requirements
The characteristic parameter of planetary row plays an important role in the whole mixing system. In this paper, a method based on the transmission efficiency of the whole working condition to solve the characteristic parameters of planetary rows is proposed.
2.2 Matching of characteristic parameters of planetary row
The basic parameters of the vehicle are shown in Table 1, and the dynamic requirements are shown in Table 2. Table 1.Parameters of vehicle
(1) Characteristics of rear planetary row
Gross Weight
4495Kg
Curb Weight
2560Kg
Drag coefficient
0.5375
Frontal Area
6m�
Rolling resistance coefficient
0.0076+0.000056*V
Vehicle parameters
Final Drive Ratio
The rear planetary row plays a role in decelerating and increasing the torque of the motor MG2. Therefore, the selection of the characteristic parameters of the rear planetary row is mainly restricted by the maximum speed and the maximum torque of the motor MG2(Fan, J.W. et al, 2013). According to the requirement of the maximum speed of 110 km/h, the maximum speed of the motor MG2 can be calculated according to equation (2).
4.785
wheel
Rolling radius
𝜔𝜔��� =
0.376m
Dynamic performance requirements
110km/h
Maximum grade
20%@15km/h
acceleration time
0-90km/h<66 s
Overtaking acceleration
60-70km/h<10 s
�.�
∙
����� ��
∙ 30 ∙ 𝑖𝑖�� (1 + 𝑘𝑘� )
(2)
𝑅𝑅���� is the wheel radius; 𝑖𝑖�� is the main reducer gear ratio.
Table 2. The dynamic requirements Maximum speed
����
60-90 km/h<39 s
2. PARAMETERS MATCHING OF POWER SYSTEM 2.1 Engine matching The power storage device in this system uses a super capacitor, which has the characteristics of high power density and small energy density. Therefore, the engine is required to provide all the energy requirements under the steady state conditions to ensure the power capacity of the system. Based on the consideration of dynamics, steady-state conditions mainly include maximum speed cruising and maximum continuous climbing(Li,Y.2016). The engine demand power is calculated based on the maximum speed requirement. �
�
� 𝑃𝑃� = ����� �𝑚𝑚𝑚𝑚𝑚𝑚� + � 𝜌𝜌𝜌𝜌� 𝐴𝐴𝐴𝐴�� + 𝑚𝑚𝑚𝑚𝑚𝑚� �
Fig. 3. New hybrid system configuration Consider the assembly relationship of planetary rows, 𝑘𝑘� > 1.5(Han, L.J. et al, 2014). It is calculated that even when 𝑘𝑘� takes the minimum value of 1.5, the maximum rotation speed of the motor MG2 will exceed 9000 rpm, which is disadvantageous to the selection of the motor. Therefore, consider removing the rear planetary gear mechanism at the beginning of the parameter matching,as shown in Figure 3 After completing the matching and selection of the motor MG2, consider whether to add the rear planetary gear row according to the specific requirements. In this way, the maximum speed of motor MG2 should be greater than 3713 rpm.
(1)
𝑃𝑃� is the engine output power (kw); 𝑉𝑉� is the vehicle speed (m/s); 𝜂𝜂� is the transmission efficiency; 𝑓𝑓� is the rolling resistance coefficient; 𝜌𝜌 is the air density; 𝐶𝐶� is the air resistance coefficient; and A is the vehicle upwind area (m2) ; 𝑖𝑖 is the slope.
(2) Characteristics of the front planetary row The parameters of the front planetary row determine the power split ratio of the system, which in turn affects the transmission efficiency of the system(Wang, X,C. 2014). Next, the relationship between the characteristics of the front planetary row and the transmission efficiency of the system is explored.
Fig. 2.Total Engine Power for Maximum Speed Requirements 22
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Considering the assembly relationship of the planetary gears and the limitation of the motor speed, the 𝑘𝑘� value ranges from [1.5, 3].
Defining the transmission efficiency is the ratio of the output power of the system to the input power. The following relationship can be obtained from the equations of motion and the dynamic equations of the planetary mechanism: ���
���
𝜂𝜂�� =
��
= (1 + 𝑘𝑘� )𝑆𝑆𝑆𝑆 𝑆 𝑆𝑆�
�� ��
�
= (���
Solve the relationship between system efficiency and the characteristic parameters of the front planetary row. Assume that the motor efficiency is a constant value of 0.9. Use the CWTVC operating conditions required by regulations as input. Use the engine's optimal working curve strategy to calculate the comprehensive efficiency of all operating conditions corresponding to different 𝑘𝑘� values. The 𝑘𝑘� value corresponding to the optimal efficiency is finally obtained. The specific solution process is shown in Fig. 4.
(3)
��
(4) (5)
�)
23
𝜔𝜔 represents the speed; subscripts S1 and R1 represent the front planetary row sun gear and ring gear; SR is the transmission ratio of the transmission system, is defined as the input speed ratio output speed (ie engine speed divided by the ring gear speed); 𝑘𝑘� is the former planet row characteristic parameters; 𝑇𝑇 represents torque, subscripts 𝑔𝑔and 𝑒𝑒 represent motor MG1 and engine. The input power of the planetary mechanism is the engine output power on the planet carrier.
𝑃𝑃� = 𝑇𝑇� 𝜔𝜔��
(6)
In the quation, C1 represents the planet carrier of the former planet. The output power consists of mechanical power and electric power.
𝑃𝑃� = 𝑃𝑃��� + 𝑃𝑃���
(7)
Since the mechanical efficiency is relatively high with respect to the motor efficiency, in the analysis, assuming that the mechanical transmission efficiency is 100%, the mechanical power can be expressed as Equation (8).
𝑃𝑃��� =
�� �� ���
(8)
(���� )
Before and after the mechanical point, as the speed or torque direction of the two motors changes, the expression of the electric power also changes. Before the mechanical point, the electrical power can be expressed as:
𝑃𝑃��� = 𝑇𝑇� 𝜔𝜔�� 𝜂𝜂� 𝜂𝜂�
(9)
𝜂𝜂� and 𝜂𝜂� are the efficiencies of the motors MG1 and MG2, respectively. The efficiency of the drive train at the mechanical point can be expressed as:
𝜂𝜂�� =
�� ��
=
�� �� �� ⁄(���� )��� �� �� �� �� ��
Fig. 4. Flowchart for calculating parameters of front planetary row In the figure above, when the required power is less than the minimum power Popt_eng_min on the engine's optimal operating curve, the pure electric mode is used, the system overall efficiency is the electrical system efficiency; when the demand power is greater than the maximum power Popt_eng_max on the engine's optimal working curve, the engine Working at the maximum power point of the optimal working curve, the lack of demand power is supplemented by the motor; in other cases, the engine works on the optimal working curve. All work points in all operating conditions have been determined, and the drive system efficiency and engine efficiency have been calculated based on the system transmission characteristics (Eqs. 9 and 11), and the overall work efficiency of the system has been obtained.
(10)
Equation (8) can be further rewritten as:
𝜂𝜂�� =
�� �[(���� )��� � ]�� �� ��(���� )
(11)
After the mechanical point, the electrical power becomes:
𝑃𝑃��� =
�� ��� �� ��
(12)
The same system drive efficiency expression can be obtained as:
𝜂𝜂�� =
�� �[(���� )����� ]∕�� ∕�� ��(���� )
Perform optimization calculations on different operating conditions and comprehensive conditions of C-WTVC drive
(13)
23
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cycle (4:4:2 according to GB/T 27840-2011, 4495kg feature ratio of urban, roadway, and high-speed parts of trucks). The results are shown in Table.3. Table. 3 Optimized eigenvalues drive cycle
Optimized k1
C-WTVC Urban
3
C-WTVC Roadway
2.26
C-WTVC Freeway
1.5
C-WTVC （4:4:2）
1.64
Fig. 7. 0-90km/h limit acceleration of power variation of each component According to the operating conditions of the motors MG1 and MG2 in the above operating conditions, peak points such as maximum rotation speed, maximum torque, and maximum power can be extracted, as shown in Table 4.
The final parameter of the front planetary row was 1.64. 3. MOTOR MATCHING
Table. 4 Peak acceleration motor operation point 0~90km/h
After the characteristic parameters of the engine and the planetary platoon are determined, based on a reasonable vehicle control strategy, the parameters of the motor can be determined according to the dynamic requirements and operating characteristics of the logistics vehicle. (Zhang, Z.L.et al, 2013). 3.1 Extreme acceleration conditions According to the engine external characteristics strategy, the engine is controlled on the external characteristic curve. After the engine operating point is obtained, the operating points of the motors MG1, MG2 can be calculated separately according to the dynamic relationship of the system. The required speed, torque and power of the motors MG1 and MG2 during the acceleration acceleration process are calculated, as shown in Fig. 5, Fig. 6 and Fig. 7.
Compone nts
Maximum torque
Maximum speed
Maximum power
MG1
140Nm/785r pm
110Nm/2983r pm
-34.4kW/2983rpm
MG2
255.8Nm/25r pm
113Nm/3038 rpm
36.1kW/3038rpm
3.2 Overtaking acceleration conditions The acceleration curve obtained by the above fitting includes acceleration sections of 60-70 km/h and 60-90 km/h, as shown in Fig .8.Among them, the acceleration time of 60-70km/h is less than 10s, and the acceleration time of 60-90km/h is less than 27s, which satisfies the dynamic requirements for overtaking acceleration. Therefore, the power system that satisfies the above extreme acceleration conditions can also meet the overtaking acceleration requirements.
Fig. 5. 0-90km/h limit acceleration power source speed
Fig. 8. Overtaking acceleration curve of 0-90 km/h 3.3 Maximum climbing conditions According to the requirements, the operating points of the motors MG1 and MG2 at a 20% gradient of 15 km/h are calculated, as shown in Figs. 9-11. Fig. 6. 0-90km/h limit acceleration power source torque
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25
Fig. 9. Maximum speed of power source for climbing conditions
Fig.12.C-WTVC power source speed (MG1 maximum positive speed)
Fig. 10. Maximum power source torque for climbing conditions
Then calculate the MG1 maximum negative speed. Assuming that the engine is off during braking, and because the engine's drag torque is relatively large, it is also assumed that the engine speed drops to zero after the engine is turned off. Therefore, high-speed braking will cause the MG1 to have a large reverse speed, as shown in Fig. 13.Although MG1 does not output torque at this time, the maximum speed exceeds 4600 rpm. Fig. 11. Maximum power requirements for various components in climbing conditions Motor operating points in climbing conditions are shown in Table 5. Table.5 climbing conditions Components
Torque/Nm
Speed/rpm
Power/kW
Engine
300.7
1331
41.9
MG1
-113.9
2683
-32
MG2
606.9
506
32
Super capacitor
--
--
7
Fig.13.C-WTVC power source speed (MG1 maximum negative speed) 3.4.2 Torque requirement In the case of C-WTVC, the required torque of the torque of each power source is shown in Fig. 14, in which the maximum driving torque of MG2 appears in the pure electric mode, which is 427 Nm, and does not exceed the peak performance of the climbing performance for MG2.The maximum braking torque of MG2 is -398Nm. Demand torque of MG1 is a maximum of -134Nm, which is also less than the requirement of climbing performance
3.4 Typical driving conditions C-WTVC In addition to the above power performance requirements, motors MG1, MG2 should also be able to meet the power requirements under typical driving conditions(Tian, B.C.2011). As mentioned earlier, this model should use 4:4:2 C-WTVC operating conditions. 3.4.1 Speed requirement The maximum speed of MG2 is positively related to the vehicle speed. The maximum speed of C-WTVC operating conditions is less than 110km/h, so the maximum speed of MG2 will not exceed 3800rpm. The MG1 speed is affected by both the vehicle speed and the engine speed. First, in the case of low-speed and high-torque driving, due to the high engine speed (the demand power on the optimal working curve is approximately proportional to the speed), the ring gear rotates at a low speed, and the MG1 will have a large forward speed, as shown in Fig. 12. The maximum forward speed of MG1 does not exceed 2500 rpm.
Fig. 14. C-WTVC power source demand torque 3.4.3 Power requirement The power demand of the system and each power source under C-WTVC is shown in Fig. 15. The maximum power of MG1 does not exceed 25 kW, the maximum power of MG2 does not exceed 20 kW, and the maximum power of super capacitors does not exceed 20 kW. The previous match is reasonable.
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IFAC E-CoSM 2018 26 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
charge/discharge efficiency is 0.9, the effective power of the supercapacitor should be greater than 0.23 kWh. 0.1
Battery Energy/kWh
0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25
Fig. 15. Power demand of C-WTVC power source
Hybrid Logistics Vehicle >37 kw
Peak torque
>610 Nm
Base speed
>506rpm
Maximum speed
>3800rpm
peak power
>35 kw
Base speed
>780rpm
Maximum speed
>4600rpm
MG2
MG1
400
600
800 1000 Time/s
1200
1400
1600
1800
Further, statistics are made on each kinematic segment in the working condition under the current electric power usage condition, and the change of the electric energy in each kinematic segment is shown in Fig. 18, and the maximum is 0.12 kWh.Within each kinematics segment, the maximum discharged electric energy of the super capacitor discharge is shown in the figure below. The maximum value is 0.18 kWh, as shown in Fig. 19.
Table.6 Matching Results of Motors MG1 and MG2
peak power
200
Fig. 17.Changes in supercapacitor energy in cycling conditions
Based on the above calculation results, the design requirements for motors MG1 and MG2 are summarized as follows:
Components
0
4. MATCHING SUPERCAPACITORS
Fig. 18. Change in electrical energy per kinematic fragment
The supercapacitor's power and energy should be able to meet the power and energy requirements in the operating conditions and extreme acceleration conditions. 4.1 Energy Matching First, according to the calculation results under the extreme acceleration condition, the supercapacitor energy change in the entire acceleration condition is shown in Fig. 16. At the time of the termination of acceleration, the supercapacitor emits 0.1126kWh of electric energy.
Fig. 19. Super capacitor discharges maximum power for each kinematic fragment In summary, the energy of the super capacitor should be greater than 0.23 kWh. 4.2 Power Matching According to Fig. 16, the maximum required power of the super capacitor in the limit acceleration condition can be found to be 26.64kw.
Fig.16. Changes in supercapacitor energy in extreme acceleration conditions
Analyze the power variation of the super capacitor in the calculation of operating conditions, as shown in Fig. 20.The super capacitor discharge power is within 20kW, and the maximum power is in the case of regenerative braking. According to the maximum power limit of the MG2, the maximum recovery power of the supercapacitor is 37kW.
The second,super capacitor should be able to meet operating conditions, including requiring as much regenerative braking energy as possible during operation.The power usage in the entire operating condition is shown in Fig. 17. It can be seen that the maximum power change of the supercapacitor is 0.21 kWh. In the initial stage, assuming the supercapacitor
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IFAC E-CoSM 2018 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
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Table.7 Simulation of the dynamic performance Project
parameters
Maximum speed
115km/h
0-90km/h acceleration time (s)
32.3
60-70km/h
4.8
acceleration time (s)
Fig. 20. C-WTVC drive cycle super capacitor demand power
60-90km/h
In summary, the power of the super capacitor should be greater than 37 kW.
acceleration time (s)
21.8
Maximum climbing grade
20%@15km/h
5. SIMULATION 5.2 Economic verification
According to the matching parameters, the simulation model is established in Cruise.
The fuel consumptions of the EVT logistics vehicle and the conventional vehicle were simulated separately in the CWTVC city section, highway segment and high-speed segment. The statistics are shown in Table 8. Table.8 EVT Logistics Vehicle Fuel Economy
Drive cycle
CWTVC urban
Fig. 21. Simulation model The simulation results and SOC changes of the speed of the EVT logistics vehicle under the C-WTVC operating conditions are shown in Fig. 22. It can be seen that the matched EVT logistics vehicle has good speed following the C-WTVC operating conditions, and the SOC can maintain balance in the entire working condition and remains at more than 50% to meet the conditions of use.
CWTVC roadway
CWTVC freeway
CWTVC
Fuel Consumptio n (L/100 km)
electricit y consum ption
Comprehe nsive Fuel Consumpt ion (L/100km)
Fuelsaving ration
Traditional vehicle
14
/
14.00
/
EVT
7.94
-1.63
7.40
47.14%
Traditional vehicle
11.94
/
11.94
/
EVT
9.09
1.96
9.74
18.43%
Traditional vehicle
14.42
/
14.42
/
EVT
11.22
-0.12
11.18
22.47%
Traditional vehicle
/
/
13.26
/
EVT
/
/
9.09
31.45%
Vehicle Type
(kWh/1 00km)
The hybrid fuel vehicle obtains a more prominent fuel saving rate under C-WTVC cycle condition, and the comprehensive fuel saving rate is 31.45%.It is proved that the matching optimization method has a remarkable effect. 5.3 Engine work points analysis Fig. 22. C-WTVC speed following and SOC simulation results
We use the engine optimal control strategy,Calculated the optimal operating line(OOL).Through the assistance of the motor, the engine is controlled to the optimal line to achieve the purpose of fuel saving.
5.1 Dynamic verification The dynamic performance of the logistics vehicle obtained by simulation is shown in Table 7. The matching logistics vehicle can meet the design requirements. 27
IFAC E-CoSM 2018 28 Changchun, China, September 20-22, 2018 Dafeng Song et al. / IFAC PapersOnLine 51-31 (2018) 21–28
Torque（Nm）
Nie,L.W.(2012). Parameter Matching and Control of Double Planetary Fluid Powered Hybrid Systems . Jilin University, Changchun,China. Peng, Z.Y, Qin, D.T., Duan, Z.H., Yang, Y.L.(2012) Working model analysis and parameter matching design of new hybrid vehicle. China Mechanical Engineering, 23(09): 1122-1128. Tian, B.C.(2011) Parameter Matching and Simulation of Series Hybrid City Bus Systems. Hunan University, Changsha,China. Wu, G.Q., Qin D.T., Hu J.C., Ye M.(2009).Research on Scheme and Parameter Matching of Hybrid Power Planetary Power Transmission System.Journal of Machine Design, 26(06):60-65.
Speed (r/min)
Fig. 23 Engine work points
Wang, X.C.(2014). Research on double-row planetary gear mechanism for hybrid passenger cars. Southwest Jiaotong University, Chongqing,China.
In Fig. 23, the blue line is the engine's external characteristic curve; the yellow line is the optimal operating line; the red point is the engine's operating point. It can be seen that most of the operating points of the engine are distributed along the OOL line, indicating that the control effect is good.
Yu, Y., Gao, Y., Peng, H., et al.(2009). Parametric design of power-split HEV drive train.Vehicle Power and Propulsion Conference,2009. VPPC'09. IEEE. IEEE, 2009:1058-1063. Zhang, Z.L., et al.(2013). Parameter Matching of Parallel Hybrid Hydraulic System for Urban Passenger Cars. China Journal of Highway and Transport, 26(3): 176-182.
6. CONCLUSION The planetary hybrid system is applied to the logistics vehicle. Based on the optimal solution of the eigenvalues of the transmission system based on the full operating conditions, the front planetary rows are matched to obtain the optimal parameters.According to the extreme working conditions, the primary motors are selected for the dual motors, and then the comprehensive matching of the motor's demand for the motors under typical operating conditions is considered. By dividing the working condition kinematic segments, matched super capacitors.Finally, the simulation model is established in Cruise to verify the dynamics and economy of the whole vehicle, and the rationality of the matching method is proved.
Zhang, G.L.(2010). Research on parameter matching, control strategy and simulation platform for hybrid vehicle based on driving conditions. South China University of Technology, Guangzhou,China. Malikopoulos, A. A. (2016). A multiobjective optimization framework for online stochastic optimal control in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 24(2), 440-450. Zhang, J., & Yuhu, W. U. (2018). A stochastic logical modelbased approximate solution for energy management problem of hevs. Science China(Information Sciences), 61(7), 70207.
ACKNOWLEDGEMENT This study was financially supported by the National Key Research and Development Program (2018YFB 0105900). REFERENCE Chen, Q.Q.(2017).Study on control strategy and parameter optimization of power split dual-mode hybrid bus. Jilin University, Changchun,China. Fan, J.W., Guo,Y.F., Lin,C., Huang,L.M.(2013).Parametric design of dual-motor planetary coupled transmission system for pure electric buses. Science Technology and Engineering, 13(35): 10741-10744+10749. Han, L.J, Liu, H., Wang, W.D, et al.(2014). Research on parameter matching and optimization of power split hybrid vehicle. Automobile Engineering, (8):904-910. Li,Y.,(2016)Parameter matching and optimization of power source for parallel hybrid buses. Beijing Institute of Technology, Beijing,Chian.
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