汽车工程 ›› 2022, Vol. 44 ›› Issue (8): 1218-1225.doi: 10.19562/j.chinasae.qcgc.2022.08.011

所属专题: 新能源汽车技术-电驱动&能量管理2022年

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基于PSO的新型双电机多模式驱动系统转矩分配策略优化

林歆悠(),黄强,张光吉   

  1. 福州大学机械工程及自动化学院,福州  350002
  • 收稿日期:2022-02-25 修回日期:2022-04-07 出版日期:2022-08-25 发布日期:2022-08-25
  • 通讯作者: 林歆悠 E-mail:linxinyoou@fzu.edu.cn
  • 基金资助:
    福建省自然科学基金(2020J01449);国家自然科学基金(51505086);安徽工程大学检测技术与节能装置安徽省重点实验室开放研究基金资助项目(JCKJ2021A04)

Torque Distribution Strategy Optimization of a Novel Dual-Motor Multi-Mode Drive System Using PSO Algorithm

Xinyou Lin(),Qiang Huang,Guangji Zhang   

  1. College of Mechanical Engineering and Automation,Fuzhou University,Fuzhou  350002
  • Received:2022-02-25 Revised:2022-04-07 Online:2022-08-25 Published:2022-08-25
  • Contact: Xinyou Lin E-mail:linxinyoou@fzu.edu.cn

摘要:

本文中对一款装备新型双电机多模式驱动系统的电动汽车进行转矩分配优化。根据双电机多模式驱动系统的特点,建立整车模型,划分不同模式的工作范围,在满足动力性的前提下,面向系统效率,制定基于粒子群优化算法的转矩分配与模式切换策略,并采用离线与在线相结合的方法提高系统的实时响应速度。在Matlab/Simulink建立仿真模型进行仿真并开展硬件在环试验验证,结果表明:系统的平均效率比传统的模式切换策略高3%;能耗比基于遗传算法的转矩分配策略减少11.28%。

关键词: 双电机系统, 粒子群优化算法, 转矩分配, 模式切换

Abstract:

Torque distribution optimization is carried out for an electric vehicle equipped with a novel dual-motor multi-mode drive system in this paper. According to the features of the dual-motor multi-mode drive system, a vehicle model is established, with the working range of different modes divided. On the premise of meeting the requirements of power performance, the system efficiency oriented strategies for torque distribution and mode switching based on particle swarm optimization algorithm are formulated, and the offline and online methods are combined to enhance the real-time response speed of the system. The simulation model is established in Matlab / Simulink with a simulation conducted and a hardware-in-the-loop test is carried out for verification. The results show that the average efficiency of the system is 3% higher than that with the traditional mode switching strategy, and the energy consumption is 11.28% lower than that with the torque distribution strategy based on genetic algorithm.

Key words: dual-motor system, particle swarm optimization algorithm, torque distribution, mode switching