汽车工程 ›› 2022, Vol. 44 ›› Issue (1): 64-72.doi: 10.19562/j.chinasae.qcgc.2022.01.009

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EMB的纯电动汽车制动能量回收优化控制策略研究

常九健,张煜帆()   

  1. 1.合肥工业大学智能制造技术研究院,合肥  230000
    2.合肥工业大学汽车与交通工程学院,合肥  230000
  • 收稿日期:2021-09-22 修回日期:2021-10-28 出版日期:2022-01-25 发布日期:2022-01-21
  • 通讯作者: 张煜帆 E-mail:244678736@qq.com
  • 基金资助:
    安徽省新能源汽车暨智能网联汽车创新工程(面向智能网联汽车的全线控底盘开发及测试验证)项目资助

Research on Optimization Control Strategy for Braking Energy Recovery of a Battery Electric Vehicle Based on EMB System

Jiujian Chang,Yufan Zhang()   

  1. 1.Intelligent Manufacturing Institute of Hefei University of Technology,Hefei  230000
    2.School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei  230000
  • Received:2021-09-22 Revised:2021-10-28 Online:2022-01-25 Published:2022-01-21
  • Contact: Yufan Zhang E-mail:244678736@qq.com

摘要:

为提高电动汽车制动时回收的能量,减少能源浪费,本文中提出了一种基于电子机械制动(EMB)系统的再生制动力分配策略。首先,根据制动踏板信号得到当前制动强度,结合前后轴制动力分配策略分别得到前轴、后轴制动力。然后以车速、电池SOC值和制动踏板行程为输入,再生制动占比为输出,创建模糊控制器,且以制动时回收能量最大化为优化目标,运用PSO算法优化模糊控制器。最后进行Simulink和AVL Cruise的联合仿真。结果表明,在NEDC工况下能量回收提升2.5%,在CLTC-P工况下能量回收提升1.56%。

关键词: 纯电动汽车, 制动能量回收, 电子机械制动, 粒子群优化, 联合仿真

Abstract:

In order to increase the energy recovered during electric vehicle braking and reduce energy waste, a regenerative braking force distribution method based on electro-mechanical braking (EMB) system is proposed. Firstly, the current braking strength is obtained according to the signal of brake pedal, and the braking forces of the front and rear axle are obtained respectively based on the strategy for braking force distribution between the front and rear axles. Then, the fuzzy controller is created with the vehicle speed, the battery SOC and the travel of brake pedal as inputs and the proportion of regenerative braking as output, and the fuzzy controller is optimized by using PSO algorithm, with maximizing the energy recovered during braking as optimization objective. Finally, a Simulink/AVL-Cruise joint-simulation is carried out. The results show that the energy recovered is increased by 2.5% under NEDC condition and by 1.56% under CLTC-P condition.

Key words: battery electric vehicles, braking energy recovery, electro-mechanical braking, particle swarm optimization, joint simulation