Energy Management Strategy Design for Dual-motor Coaxial Coupling Propulsion Electric City-buses

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CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy urban energy systems, 5–7 June 2018, Shanghai, China Applied Energy Symposium and Forum 2018: LowShanghai, carbon cities and urban energy systems, CUE2018, 5–7 June 2018, China CUE2018, 5–7 June 2018, Shanghai, China The 15th International Symposium on District and Cooling Energy Management Strategy Design for Heating Dual-motor Coaxial

Energy Management Strategy Design for Dual-motor Coaxial Coupling Propulsion Electric City-buses Assessing the feasibility of using the heat demand-outdoor Coupling Propulsion Electric City-buses a,b, demand forecast temperature function aa,b long-term heat Mingjiefor Zhao , Junhui Shicc,district Cheng Lin * a,b a,b, Mingjie Zhao , Junhui Shi , Cheng Lin * *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

National Engineering of Techonology,c a,b,c Laboratory foraElectric Vehicles,aSchool of Mechanical b Engineering, Beijing Institute c a National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Techonology, Beijing, 100081, China b Beijing, 100081,Beijing China Institute of Technology, Beijing, 100081, China Collaborative Innovation Center of Electric Vehicles in Beijing, a IN+ Center for Innovation, Technology Policy Research - Instituto Técnico, Rovisco 1, 1049-001 b c Collaborative Center of Electric Vehicles in Beijing, Beijing Institute ofAv. Technology, Beijing, 100081,Lisbon, China Portugal State Innovation Keyb Laboratory ofand Automotive Safety and Energy,Superior Tsinghua University, Beijing, Pais 100081, China Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay,100081, France China c State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France a

I. Andrić

Abstract Abstract Abstract A practical dual-motor coaxial coupling propulsion system for electric city-buses and an extraction method for optimal energy A practical dual-motor coupling propulsionconfiguration system for electric city-buses and an extraction method forbus optimal energy management strategy arecoaxial proposed. The powertrain of the dual-motor coaxial coupling propulsion (DMCEB) is District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the management proposed. The powertrain configuration the dual-motor state coaxial propulsion bus (DMCEB) is illustrated. Tostrategy researcharethe energy-saving strategy of DMCEB, theofcomprehensive andcoupling cost functions are established. Then greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat illustrated. To research(DP) the energy-saving strategytoof DMCEB, theoptimal comprehensive state and cost functions are established. Then dynamic programming algorithm is adopted find the global energy management strategy including the transmission sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, dynamic programming (DP) algorithm adopted to find the global optimal energy management strategy including the implemented transmission shift schedule, the torque split ratio andisthe operating points of the two motors. Since the DP-based strategy is hardly prolonging the investment return period. shift schedule, the torquea split and the operatingoptimal points of the two motors. the DP-based strategy is hardly implemented in real vehicle directly, novelratio application-oriented rule-based strategySince is extracted from the DP results by utilizing nonThe main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand in real support vehicle directly, a novel (SVM) application-oriented optimalclustering rule-basedand strategy is extracted from the DP methods. results by The utilizing nonlinear vector machine classifier, K-means piecewise polynomial fitting extracted forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 linear machine (SVM) classifier, K-means clustering and piecewise Theand extracted strategysupport is usedvector to redesign and improve the preliminary rule-based strategy, which canpolynomial decouple thefitting onlinemethods. calculation offline buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district strategy is used to The redesign and improve preliminary strategy, which can decouple the online and offline application parts. simulation results the demonstrate thatrule-based the extracted strategy is only around 10% worse calculation than that based on DP renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were application parts. The results demonstrate extractedpolynomial strategy is form. only around 10% worse than that based on DP results. However, it cansimulation be executed online easily due that to itsthe simplified compared with results from a dynamic heat demand model, previously developed and validated by the authors. results. However, it can be executed online easily due to its simplified polynomial form. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Copyright © 2018 Elsevier Ltd. All rights reserved. (the error©in2018 annual demand than 20% for all weather scenarios considered). However, after introducing renovation Copyright Elsevier Ltd.was Alllower rights Copyright © 2018 Elsevier Ltd. All rights reserved. reserved. Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low Selection under responsibility the scientific therenovation CUE2018-Applied Symposium and scenarios,and thepeer-review error value increased up to 59.5%of(depending on committee the weatherofand scenarios Energy combination considered). Selection andand peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities urban energy systems, CUE2018. Forum 2018: Low carbon cities and urban energy systems. The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the carbon cities and urban energy systems, CUE2018. decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Keywords: Energy management strategy; Dual-motor coupling propulsion system; Dynamic programing; Optimal rule-based strategy extraction renovation scenarios considered). the othercoupling hand, function increased for 7.8-12.7% decadestrategy (depending on the Keywords: Energy management strategy;On Dual-motor propulsionintercept system; Dynamic programing; Optimalper rule-based extraction coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +86-10-6891-3992; fax: +86-10-6891-3992. * Corresponding author. Tel.: +86-10-6891-3992; E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate changefax: +86-10-6891-3992. E-mail address: [email protected]

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection and peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.212

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Mingjie Zhao et al. / Energy Procedia 152 (2018) 568–573 Author name / Energy Procedia 00 (2018) 000–000

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1. Introduction Development of battery electric buses (BEBs) is recognized as one of the most promising avenues for the public transportation in cities owing to its renewable electricity and zero emissions [1]. However, the conventional single motor integrated with automated mechanical transmission (AMT) system has traction interruption resulting in a bad ride comfort and is limited in energy optimization due to the only one propulsion unit [2]. Another typical architecture of BEBs is specially consist of two motors with a planetary gear train (PGT) in a speed-coupling way [3]. However, it seems improper for low speed but heavy load city-buses because of its lack of transmission capacity. Besides a suitable powertrain configuration, energy management strategy is also a critical factor that will influence the energy consumption and dynamic performance of BEBs. Since rule-based strategies can barely lead to the optimal operation, many studies have been conducted to explore the global optimization-based energy management strategy. Dynamic programming (DP) is one of the most effective algorithms that can be used to find the global optimal results [4]. However, the huge computation burdens of DP hinder its online application potential [5]. Though the DP results are occasionally utilized as reference to derive practical control rules, the cursory intuitive extraction methods, without any discussion on accuracy, can hardly guarantee an excellent energy-saving effect [6]. In this paper, a practical dual-motor coaxial coupling propulsion bus (DMCEB) and a rational extraction method for optimal energy management strategy are presented. In section 2, the configuration of DMCEB is illustrated. DP method is implemented to explore the optimal results in section 3. Section 4 introduces a novel extraction method used to establish an improved rule-based strategy according to the DP results. Simulation results are compared and discussed in Section 5 before conclusions drawn in the final section. 2. Configuration of the DMCEB powertrain The architecture of the DMCEB powertrain is shown in Fig. 1, where an auxiliary motor (AM) equipped with a two-speed transmission and a traction motor (TM) connected directly to the main reducer are coaxially arranged to drive the vehicle in a torque-coupling way. Since the TM can always supply considerable power, the interruption impact in traditional shift process can be eliminated. The original data of the main components are determined by experiments in look-up table form to establish the model of the powertrain. Vehicle Controller Traction Controller

CAN bus

Electric cable Motor Controller

BMS Battery Pack Differential

AM Two-speed Transmission

TM Shift Clutch Main Reducer

Fig. 1. The architecture of the powertrain of the dual-motor coaxial coupling propulsion bus. Table 1. Main parameters of the dual motor coaxial coupling propulsion bus. Parameters

Symbol

Values

Gross and curb weight

M, m

18000kg, 15600kg

Tire rolling radius

r

0.465m

Transmission and Main reducer ratio

ig , i0

2.5/1, 5.24

Driving resistance coefficient

a, b, c

814.2N, 7.244N/(km/h), 0.261N/(km/h)2

Overall rotating mass coefficient and efficiency

δ, η

1.02, 0.92

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3

3. Offline optimal energy management based on dynamic programming Dynamic programming based on Bellman’s principle of optimality has advantages in finding the optimal control law at each stage to minimize the global energy cost effectively [7]. It firstly calculates the values of cost function based on the model of DMCEB and attaches all inputs to the corresponding state and control variables from the end of the discrete sequence of the problem. Therefore the global optimization problem can be divided into a sequence of secondary problems backward from the terminal stage and can be solved forward to determine the optimal control trace. To simplify the control variables as possible, only torque split ratio (TSR) of the two motors and transmission shift command are selected. For this discrete optimal problem, a penalty term Ω = 5 is added in order to reduce frequent gear shift phenomenon. Then the optimization goal is to find the control sequence to minimize the battery energy cost considering the frequency of shift and the relevant functions can be expressed as:  xk 1  f ( xk , uk )  x  [ SOC , gear ] k k  k u [ TSR , shift   k k k]  N 1 N 1 J  L( xk , uk )  ( ECk (uk , k )    shiftk )    k 0 k 0 

(1)

To solve the above recursive Eq. (1) backward, the state ‫ݔ‬௞ and control variables ‫ݑ‬௞ should be discretized into finite grids and interpolation method is utilized to evaluate the values if ‫ݔ‬௞ାଵ does not fall on the grid points. Here the gear shift control can only be upshift, sustain and downshift, so the grid is integer and fixed. The grid of other variables should be set as finer as possible to achieve more significant results. To avoid exponential computational burden even in offline solving process, an approximately grid accuracy is determined. 4. Extraction method for online energy management strategy 70 60 50

SOC

40 30 20 10 0

0

200

400

600

800

Time (s)

(a)

1000

1200

1400

(b)

Fig. 2. (a) The Chinese typical city bus drive cycle (CCBC); (b) SOC variation of CCBC under DP control

The Chinese typical city bus drive cycle (CCBC) as shown in Fig. 2 (a), is implemented in the DP procedure described above, with a driving distance of 5.89 km and a driving duration of 1314 s. The simulation result of SOC variation under DP control running in CCBC is shown in Fig. 2 (b). The initial SOC is set to 90.0% and in the end of the cycle it only reduces to 87.9%. To apply the DP results, an online available control law determined by pedal position and velocity should be extracted and used to recalibrate the preliminary rule-based strategy. Moreover, since the route of a city-bus is always determined in advance and fixed, the extracted strategy from the specific driving cycle is quite suitable for public transportation and can reach the optimal energy-saving effect in real vehicle. 4.1. TSR function extraction Here take the acceleration process as an example. As shown in Fig. 3, it’s hard to find the TSR analytic expressions and shift rules accurately, since they are related to both the target torque and velocity and are strongly irregular.

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(a)

571

(b)

Fig. 3. Working points distribution of (a) TSR in acceleration process; (b) gear in acceleration process.

The basic idea of the TSR extraction method is to separate the relevant points in small scale using clustering algorithm and determine the boundaries by classification algorithm. Then in each new cluster, piecewise TSR polynomial expressions with high precise and low order can be extracted. The specific procedures are as follows: Table 2. The procedures of TSR extraction based on K-means and SVM algorithms. Step (1) (2)

Procedure Content Calculate and evaluate the correlation coefficients ρ between original TSR and velocity, pedal position. If both of the results show weak correlation, then the points need to be divided in small scale to improve the degree of correlation in new cluster. K-means clustering method is employed to separate the points and the number of cluster centers k is initialized as k = 2 illustrated in Fig. 4 (a) . The ρ will be improved a lot in each cluster. If the TSR and pedal position (or velocity) in both clusters show a preferable correlation (ρ > 0.5), a curve fitting (first order polynomial) between TSR and pedal position (or velocity) could be reasonable.

(3)

According to step (2), lower order polynomial fitting will be tried first. To guarantee the practicability in vehicular control chips, the order is better limited to quadratic. However, the curve fitting in accelerating process is not precise enough and then quadratic surface fitting is executed as shown in Fig. 5, which can achieve an excellent fitting results.

(4)

If the goodness of fitting is still not satisfied, then let the number of cluster centers k = k + 1 and repeat step (2) and step (3).

(5)

As the TSR scatter has been clustered into two or more parts, a classification line should be determined in v - α plane as shown in. Fig. 4 (b). Here the boundary in accelerating process is solved by SVM classifier and then the simple fitting lines are abstracted to simplify the computational complexity.

(6)

The TSR expression is eventually described as piecewise functions in which TSR is the dependent variable, and all the related control information can be determined above.

1 0.8 0.6 0.4 0.2

First cluster of data Second cluster of data Cluster center

1 No 0.8 rma 0.6 lize 0.4 d v 0.2 elo city 0

1

(a)

0.8

0.6

0.4

0.2

0

(b)

Fig. 4. (a) Two clusters of TSR scatter in acceleration process; (b) boundary of two clusters in acceleration process.

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(a)

5

(b)

Fig. 5. Surface fitting of TSR in (a) first cluster with R2 = 0.976, RMSE = 0.012; (b) second cluster with R2 = 0.955, RMSE = 0.033.

As shown in Fig. 5, the goodness of fitting is quite satisfied with both of the R2 ≥ 0.95 and RMSE ≤ 0.05. Besides, the expressions of the TSR are only quadratic piecewise polynomial that can be calculated with low computational burden online by common microcontroller rapidly. 4.2. Transmission gear shift schedule extraction The shift schedule extraction can be equivalent to a linear inseparable binary classification problem. Many tools have been developed to solve the classification problem, and support vector machine (SVM) classifier is an excellent kernel-based tool for binary data classification [8]. Considering the intercross points in Fig. 3 (b), the boundary of the points is ambiguous, namely a strict classification hyperplane does not exist. Thus a relaxation factor can be adopted to relax the constraints [9]. The classifier hyperplane illustrated in Fig. 6 (a) can be set as a reference to extract the upshift line. To avoid shift cycle in the gear shift process, a downshift line with a proper interval can be drawn as shown in Fig. 6 (b). 0.7

Gear 1 working point Gear 2 working point Support vector Classifier hyperplane

0.6 0.5

0.7 0.6 0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0

10

20

30

Velocity (km/h)

(a)

40

50

60

0

Gear 1 working point Gear 2 working point Downshift command Upshift command

0

10

20

30

Velocity (km/h)

40

50

60

(b)

Fig. 6. (a) Classification hyperplane under RBF kernel with γ = 4, C = 100; (b) accelerating shift schedule extracted from DP.

5. Comparison between results of DP-based strategy and extracted rule-based strategy A simulation model of the DMCEB is built in AVL Cruise software to validate the extracted TSR and shift rules. Fig. 7 show the SOC trace results of the strategies based on DP algorithm and the aforementioned extracted rules in the same trend. The SOC is initialized as 90.0% and the variations of SOC for CCBC driving cycle are both gradually decreasing with frequent fluctuations. The DMCEB consumed only 2.12% SOC in CCBC cycle under the DP-based strategy whereas 2.23 % SOC is necessary that under the improved rule-based strategy, namely the online improved strategy can reach more than 95% effect of DP method. The deviation between these two strategies is mainly caused by extraction error. However, as the order of the extracted polynomial expression has been limited in quadratic, the online operational capability of extracted strategy is much more prominent than DP strategy.

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SOC

6

Fig. 7. SOC traces of strategies based on DP results and extracted rule-based strategy.

6. Conclusion  This paper proposes a practical dual-motor coaxial coupling propulsion system for electric city-buses, which can eliminate the traction interruption in traditional powertrain and expand the potential of energy management. DP method is employed to obtain the global optimal operating points of the two motors and transmission over CCBC driving cycle considering the shift frequency.  A novel extraction method for TSR expression utilizing K-means clustering, piecewise polynomial fitting and non-linear support vector machine classifier is developed, meanwhile correlation coefficient, goodness of fit and application effect are discussed to guarantee the accuracy.  The shift schedule extraction problem is converted to a binary classification problem and SVM algorithm is used to find the hyperplane of different gear status, which can guide the design of online upshift and downshift lines.  Eventually, an improved rule-based strategy is determined including TSR optimal rules and shift schedules in a form of quadratic polynomial. The simulation results demonstrate that the extracted rules can reach 95% effect of that under DP. In general, the proposed configuration and novel extraction method provide a reasonable solution to obtain online available optimal rules, especially suits the fixed-route city bus driving situations. Acknowledgements This work was supported by the National Key Technology Research and Development Program of China (2017YFB0103801). Any opinions expressed in this paper are solely those of the authors and do not represent those of the sponsors. References [1] Nanaki EA, Koroneos CJ. Comparative economic and environmental analysis of conventional, hybrid and electric vehicles – the case study of Greece. J CLEAN PROD. 2013;53:261-266. [2] Tseng C, Yu C. Advanced shifting control of synchronizer mechanisms for clutchless automatic manual transmission in an electric vehicle. MECH MACH THEORY. 2015;84:37-56. [3] Zhang S, Xiong R, Zhang C, Sun F. An optimal structure selection and parameter design approach for a dual-motor-driven system used in an electric bus. ENERGY. 2016;96:437-448. [4] M. Sabri MF, Danapalasingam KA, Rahmat MF. A review on hybrid electric vehicles architecture and energy management strategies. Renewable and Sustainable Energy Reviews. 2016;53:1433-1442. [5] Wirasingha SG, Emadi A. Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles. IEEE T VEH TECHNOL. 2011;60:111-122. [6] Zhang S, Xiong R. Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. APPL ENERG. 2015;155:68-78. [7] Torres JL, Gonzalez R, Gimenez A, Lopez J. Energy management strategy for plug-in hybrid electric vehicles. A comparative study. APPL ENERG. 2014;113:816-824. [8] Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. NEURAL NETWORKS. 1999;12:783-789. [9] Zhao M, Shi J, Lin C, Zhang J. Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission. ENERGIES. 2018;11:325.