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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (11): 2214-2223    DOI: 10.3785/j.issn.1008-973X.2020.11.017
    
Torque distribution strategy of pure electric driving mode for dual planetary vehicle
Xiang-fei MENG1,2(),Ren-guang WANG1,*(),Yuan-li XU2
1. China Automotive Technology and Research Center Co. Ltd, Tianjin 300300, China
2. College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
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Abstract  

A control strategy of motor efficient working range was designed on the basis of analysing the energy flow characteristics of the dual planetary gear plug-in hybrid electric system, aiming at the problem of torque distribution in dual-motor pure electric drive mode. The difference of working characteristics of the two motors is utilized to achieve the purpose of complementation of the efficient working range, and protect the working efficiency of main motor under the dual-motor torque coupling mode. A dual fuzzy controller control system was designed to solve the problem that the control accuracy is low in traditional fuzzy controller. With the proposed motor working range division method, the control system was used to realize the adaptive adjustment and equivalent amplification of motor working range. The fitness function was constructed by taking the energy conversion efficiency of the drive system and the torque ripple coefficient of the dual-motor as independent variables, and the multi-objective optimization of control rules of the system was carried out based on the genetic algorithm. Simulation results showed that, among the two control strategies, the power consumption of the genetic algorithm-dual fuzzy controller control strategy was lower, the distribution of the integrated efficiency of the system was close to the optimal economic result of dynamic programming (DP), and the control of motor output torque fluctuation was also more reasonable. The comprehensive fuel consumption of 100-kilometers of vehicle of the genetic algorithm-dual fuzzy controller control strategy was 3.27% lower than that of the main motor efficient working range control strategy when the two strategies were applied to hybrid electric system.

Key words： pure electric drive mode      dual motor torque distribution      motor efficient working range      dual fuzzy control system      genetic algorithm multi-objective optimization     
Received: 05 September 2019      Published: 15 December 2020
CLC:  U 469.7  
Corresponding Authors: Ren-guang WANG     E-mail: mengxiangfei@mail.tust.edu.cn;wangrenguang@catarc.ac.cn
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Xiang-fei MENG
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Yuan-li XU
Cite this article:

Xiang-fei MENG,Ren-guang WANG,Yuan-li XU. Torque distribution strategy of pure electric driving mode for dual planetary vehicle. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2214-2223.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.11.017     OR     http://www.zjujournals.com/eng/Y2020/V54/I11/2214


双行星排汽车纯电驱动模式的转矩分配策略

针对双电机纯电驱动模式的转矩分配问题，在分析双行星排插电式混合动力系统能量流动特性的基础上，设计主电机高效工作区间控制策略. 该策略利用双电机工作特性的差异达到高效工作区间互补的目的，并在双电机转矩耦合模式下保护主电机的工作效率. 为了解决传统模糊控制器控制精度不高的问题，设计双模糊控制器控制系统，结合所提出的电机工作区间划分方法实现电机工作区间的自适应调节与等效放大. 以驱动系统能量转化效率与电机转矩脉动系数为自变量构建适应度函数，基于遗传算法对系统的控制规则进行多目标寻优. 仿真结果表明，在2种控制策略中遗传算法-双模糊控制器控制策略的耗电量更低，系统综合效率分布情况接近动态规划（DP）经济性最优结果，对电机输出转矩波动情况的控制也更加合理. 将其应用于混合动力系统，车辆百公里综合油耗较主电机高效工作区间控制策略的降低3.27%.


关键词： 纯电驱动模式,  双电机转矩分配,  电机高效工作区间,  双模糊控制系统,  遗传算法多目标寻优 
Fig.1 Structure diagram of dual planetary PHEV powertrain
Fig.2 Dual-motor efficiency MAP and most efficient curve of main motor
Fig.3 Schematic diagram of M2-EWR control strategy mode division
Fig.4 Flowchart of M2-EWR control strategy
Fig.5 Relationship between working range expansion coefficient and vehicle cycle power consumption
Fig.6 Schematic diagram of main motor working range partition
Fig.7 Schematic diagram of dual fuzzy controller control system
Fig.8 Flowchart of fuzzy control rule automatic optimization based on genetic algorithm
Fig.9 Distribution of fitness values of individuals in initial population
Fig.10 Comparison of changes in fitness values of DFCCS and TISOFCS optimal individuals
Fig.11 ORAAC genetic algorithm optimization results
Fig.12 TDC genetic algorithm optimization results
Fig.13 Speed following results of two strategies under CLTC-P condition
Fig.14 Power battery SOC change track under three control methods
%
控制方法 各综合效率区间
0.75~0.80 0.80~0.85 0.85~0.90 0.90~0.93 0.93~0.95 0.95~1.00
M2-EWR控制策略 1.37 8.17 17.56 22.48 30.28 20.14
GA-DFC控制策略 0.20 7.51 17.02 24.09 30.03 21.15
DP结果 0.20 6.74 14.88 25.26 28.76 24.16
Tab.1 Statistical table of comprehensive efficiency distribution of dual-motor system
Fig.15 Comprehensive efficiency distribution of dual-motor system under three control methods
控制方法 TM1_rip TM2_rip
M2-EWR控制策略 13.9410 10.9568
GA-DFC控制策略 7.8289 14.4511
Tab.2 Statistical table of torque ripple coefficients of dual-motor
Fig.16 Dual-motor output torque simulation results
Fig.17 Flowchart of vehicle control strategy mode switching
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