中国机械工程 ›› 2015, Vol. 26 ›› Issue (13): 1752-1759.

• 机械基础工程 • 上一篇    下一篇

基于模糊PID扭矩识别的混合动力汽车优化控制

钱立军;邱利宏;陈朋   

  1. 合肥工业大学,合肥,230009
  • 出版日期:2015-07-10 发布日期:2015-07-08
  • 基金资助:
    国家高技术研究发展计划(863计划)资助项目(SQ2010AA1122977001);2012年国家新能源汽车技术创新工程资助项目(财建[2012]1095) 

Optimal Control of a Hybrid Electric Vehicle Based on Fuzzy-PID Torque Identification

Qian Lijun;Qiu Lihong;Chen Peng   

  1. Hefei University of Technology,Hefei,230009
  • Online:2015-07-10 Published:2015-07-08
  • Supported by:
    National High-tech R&D Program of China (863 Program) (No. SQ2010AA1122977001)

摘要:

提出了一种基于模糊PID扭矩识别系数K的计算方法,并利用K对驱动模式进行初步判断。使用粒子群-蚁群组合优化算法对控制策略中的关键控制参数及部分动力系统参数进行了优化。优化后,在保证整车动力性的基础上,油耗和排放都有所降低。对优化后的控制策略进行了硬件在环仿真,仿真结果表明,基于模糊PID扭矩识别的控制策略可以实现基本的能量管理,且控制效果优于未引入模糊PID扭矩识别的控制策略。

关键词: 插电式混合动力汽车, 模糊PID, 粒子群-蚁群算法, 硬件在环仿真

Abstract:

A method wss put forward based on fuzzy-PID torque identification K, thus the drive mode of the vehicle was preliminarily decided by K. The particle swarm-ant colony algorithm was used to optimize the key parameters of the control strategy and the power components of the vehicle. After optimization, fuel consumption and emissions were reduced with power performances guaranteed. The optimized control strategy was simulated hardware in loop. The results indicate that the control strategy based on fuzzy-PID torque identification can realize basic energy management, and the control effectiveness is better than the controller without fuzzy-PID torque identification.

Key words: plug-in hybrid electric vehicle, fuzzy-PID, particle swarm-ant colony algorithm, hardware in loop(HIL) simulation

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