基于新型改进遗传算法的混合动力客车高效制动能量回收预测控制策略研究

doi:10.3901/JME.2020.18.105

机械工程学报 ›› 2020, Vol. 56 ›› Issue (18): 105-115.doi: 10.3901/JME.2020.18.105

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基于新型改进遗传算法的混合动力客车高效制动能量回收预测控制策略研究

张渊博¹, 王伟达¹, 张华³, 杨超¹, 项昌乐¹, 李亮²

1. 北京理工大学机械与车辆学院北京 100081;
2. 清华大学汽车安全与节能国家重点实验室北京 100084;
3. 内蒙古一机集团宏远电器有限公司包头 140032

收稿日期:2019-12-03 修回日期:2020-06-19 出版日期:2020-09-20 发布日期:2020-11-17
通讯作者: 王伟达(通信作者),男,1980年出生,博士,副教授,博士研究生导师。主要研究方向为混合动力车辆,机电传动控制,车辆动力学及其控制。E-mail:wangwd0430@163.com
作者简介:张渊博,男,1990年出生,博士研究生。主要研究方向为新能源汽车制动能量回收控制技术,分布式独立电驱动汽车动力学及控制。E-mail:yuanbzhang@163.com
基金资助:
国家自然科学基金资助项目（51575043，U1764257，U1564210）。

Research on Modified Genetic Algorithm-based High Efficiency Predictive Regenerative Braking Control Strategy for Hybrid Electric Bus

ZHANG Yuanbo¹, WANG Weida¹, ZHANG Hua³, YANG Chao¹, XIANG Changle¹, LI Liang²

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
2. The State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084;
3. Inner Mongolia First Mechinery Group Hongyuan Electric Appliance Co., Ltd., Baotou 140032

Received:2019-12-03 Revised:2020-06-19 Online:2020-09-20 Published:2020-11-17

摘要/Abstract

摘要： 制动能量回收是提升混合动力客车燃油经济性的核心技术之一。然而基于传统客车机械制动系统与制动能量回收系统集成的混合制动系统，在多种复杂市区、郊区甚至极限工况下，如何通过合理分配再生制动力矩和摩擦机械制动力矩，保证整车稳定性和经济性均衡最优，仍为新能源汽车领域亟待解决的难题。为此，提出一种基于新型改进遗传算法的混合动力客车高效制动能量回收控制策略。结合混合制动系统结构与动力学特性，搭建7自由度整车纵向动力学模型；考虑轮胎在临界稳定区域的高度非线性以及制动过程中稳定性、经济性等性能要求的多目标特性，采用遗传算法对有限时域内的前后轴机械制动力矩及电机制动力矩的最优分配问题进行预测求解，并采取滚动优化策略实现整个制动过程的最优控制，同时为了防止在预测域内收敛于局部最优解，设计多子种群各自迭代并组合优化的方法对遗传算法进行改进；基于多维表格和最近点的方法对该控制策略进行实时化处理，并完成仿真与硬件在环试验。试验结果表明提出策略在保证整车稳定性的同时，较实车控制器中采用的规则式控制策略，提升15%的制动能量回收率。

关键词: 混合动力客车, 制动能量回收, 改进遗传算法, 预测控制策略

Abstract: Regenerative braking technology of electric vehicle is one of the main technologies to improve its economy. However, based on the hybrid braking system which integrates traditional mechanical braking system and regenerative braking system, how to reasonably distribute regenerative braking torque and friction mechanical braking torque to ensure the overall optimization of vehicle stability and economy in multiple complex working conditions is still a challenge. To solve this problem, an efficiency predictive regenerative braking control strategy based on modified genetic algorithm is proposed. Firstly, a 7 degree of freedom longitudinal vehicle dynamic model is built according to the braking system mechanical structure and dynamic characteristics. Then, considering the high non-linearity of tire in the unstable region and the multi-objective characteristics of stability, economy and other performance requirements in the braking process, the genetic algorithm is used to solve the optimal braking torque distribution problem in finite time domain, and the rolling optimization method is adopted to achieve the optimal control of the whole braking process. At the same time, in order to prevent that calculation result converges to local optimal solution, some modified methods are designed to improve the genetic algorithm; finally, based on the multi-dimensional table and the nearest point method, the real-time calculation of control strategy is realized, and the simulation and hardware in the loop tests are completed. The test results show that the proposed strategy can not only ensure the stability of the whole vehicle, but also improve the braking energy recovery by 15% compared with the regular control strategy which is used in the real vehicle controller.

Key words: hybrid electric bus, regenerative braking, modified genetic algorithm, predictive control

中图分类号:

U469

张渊博, 王伟达, 张华, 杨超, 项昌乐, 李亮. 基于新型改进遗传算法的混合动力客车高效制动能量回收预测控制策略研究[J]. 机械工程学报, 2020, 56(18): 105-115.

ZHANG Yuanbo, WANG Weida, ZHANG Hua, YANG Chao, XIANG Changle, LI Liang. Research on Modified Genetic Algorithm-based High Efficiency Predictive Regenerative Braking Control Strategy for Hybrid Electric Bus[J]. Journal of Mechanical Engineering, 2020, 56(18): 105-115.

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基于新型改进遗传算法的混合动力客车高效制动能量回收预测控制策略研究

Research on Modified Genetic Algorithm-based High Efficiency Predictive Regenerative Braking Control Strategy for Hybrid Electric Bus

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