Control strategy for regenerative braking based on fuzzy pattern recognition in dual-motor electric vehicles

doi:10.13196/j.cims.2020.04.001

›› 2020, Vol. 26 ›› Issue (第4): 871-881.DOI: 10.13196/j.cims.2020.04.001

Online:2020-04-30 Published:2020-04-30
Supported by:
Project supported by the National Natural Science Foundation,China(No.51475059),and the Innovation Research Team Development Program of the Ministry of Education,China(No.IRT_15R64).

基于模式模糊识别的电动车双电机系统再生制动控制

李聪波,易茜⁺,胡芮,朱道光,胡捷

重庆大学机械传动国家重点实验室

基金资助:
国家自然科学基金资助项目(51475059);教育部“创新团队发展计划”资助项目(IRT_15R64)。

Abstract

Abstract: For a dual-motor electric vehicle,the control strategy of regenerative braking which two motors work at high power efficient area was proposed to improve the recovery of regenerative braking energy and guarantee the smoothness of mode switching.Firstly,based on power efficient of two motors,four regenerative braking modes were analyzed.Secondly,by using the fuzzy control principle,a fuzzy recognizer which outputs the regenerative braking force distribution coefficient was designed.The regenerative braking mode was matched according to the fuzzy recognizer.Then,in order to ensure the smoothness of mode switching,the regenerative braking mode was switched according to the jerk.Furthermore,the control strategy for regenerative braking was proposed after the braking force distribution.Finally,based on MATLAB/SIMULINK simulation platform,the vehicle was simulated and analyzed.The simulation revealed that the braking energy recovery rate was 27.9%.Compared with regenerative braking control strategy without braking mode fuzzy recognition,the energy recovery is increased by 14.3%.The jerk of mode switching meets the German standard at the same time.

Key words: electric vehicle, dual-motor, regenerative braking mode, fuzzy control, MATLAB/SIMULINK simulation

摘要： 针对双电机动力耦合纯电动汽车,提出一种基于模式模糊识别的再生制动控制策略,以保证双电机在高效区域工作,提高再生制动能量回收率,同时保证模式切换的平稳性。首先基于电机效率MAP图划分双电机电动汽车的四种再生制动模式;其次利用模糊控制原理设计输出为最大再生制动力分配系数的模糊识别器,根据再生制动力的大小及车速匹配到合适的再生制动模式;然后为保证模式切换的平稳性,根据再生制动模式切换的冲击度值判断是否进行模式切换;进而对前后轮制动力进行分配,制定再生制动控制策略。最后基于MATLAB/SIMULINK仿真平台进行了整车仿真分析,结果表明,电动汽车双电机系统采用该再生制动控制策略,可使再生制动能量利用率达到27.9%,比只利用功率较小的电机进行发电的再生制动控制策略能量回收率提高14.3%,同时模式切换冲击度低于德国标准。

关键词: 电动汽车, 双电机, 再生制动模式, 模糊控制, MATLAB/SIMULINK仿真

CLC Number:

U462.3

李聪波,易茜,胡芮,朱道光,胡捷. 基于模式模糊识别的电动车双电机系统再生制动控制[J]. 计算机集成制造系统, 2020, 26(第4): 871-881.

[1]	. Dynamic evaluation of lightweight automobile life cycle based on Vensim software [J]. , 2020, 26(第4): 954-969.
[2]	. The determination of comprehensive importance ratings of electric vehicle's technical characteristics based on QFD under uncertain context [J]. , 2020, 26(第1): 103-113.
[3]	. Integrated optimization of two-speed transmission system parameters for electric vehicle without power interruption [J]. , 2020, 26(9): 2453-2462.
[4]	. Multi-objective optimization of gear decision for electric vehicle during regenerative braking [J]. , 2020, 26(5期): 1246-1256.
[5]	. Integrated approach of two-speed power system parameter and control strategy optimization for electric vehicle [J]. , 2019, 25(第7): 1706-1716.
[6]	. Parameters optimization of 2-speed EV powertrain for energy consumption [J]. , 2018, 24(第6): 1327-1336.
[7]	. Control strategy of dual-motor EV powertrain for energy consumption [J]. , 2018, 24(第5): 1098-1109.
[8]	. Parameters optimization of dual-motor EV power train for energy consumption [J]. , 2017, 23(第8期): 1620-1628.
[9]	. Simulation research on fuzzy control of PHEV powertrain engine [J]. , 2016, 22(第11期): 2573-2579.
[10]	LIANG Jie,, CHEN Li, LIANG Pin. Neural network adaptive control and vibration hierarchical fuzzy control of flexible arm space robot [J]. , 2012, 18(09): 0-0.
[11]	FAN Yun-sheng,, GUO Chen,, WANG Guo-feng. Preloading and measuring of differential bearing based on intelligent control [J]. , 2012, 18(06): 0-0.
[12]	HOU Shi-wang，，TONG Shu-rong. Fuzzy number-based uncertain quality control chart and type selection [J]. , 2012, 18(02): 0-0.
[13]	DENG Chao，WU Jun，LIU Qian. Position precision compensation method based on fuzzy control [J]. , 2010, 16(03): 0-0.
[14]	MO Ju-hua, HUANG Min,, WANG Xing-wei. Optimal design of serial production control system based on fuzzy control and genetic algorithm [J]. , 2009, 15(11): 0-0.
[15]	XIAO Ren-bin, CAI Zheng-ying. Fuzzy cost control of closed-loop quality chain under uncertain quality level [J]. , 2009, 15(06): 0-0.