系统仿真学报 ›› 2019, Vol. 31 ›› Issue (9): 1860-1867.doi: 10.16182/j.issn1004731x.joss.17-0312

• 仿真应用工程 • 上一篇    下一篇

基于DP算法的复合电源混合动力系统控制优化

邓涛, 余浩源, 徐彬   

  1. 重庆交通大学 机电与车辆工程学院,重庆 400074
  • 收稿日期:2017-06-30 修回日期:2017-08-24 发布日期:2019-12-12
  • 作者简介:邓涛(1982-),男,江西,博士,教授,研究方向为混合动力电动汽车控制;余浩源(1994-),男,重庆,硕士生,研究方向为建模仿真;徐彬(1990-)男,江苏,硕士生,研究方向为混合动力汽车能量管理。
  • 基金资助:
    国家自然科学基金(51305473),中国博士后科学基金(2014M552317),重庆市博士后研究人员科研项目(xm201432)

Control Optimization of Hybrid System with Hybrid Power Based on DP Algorithm

Deng Tao, Yu Haoyuan, Xu Bin   

  1. School of Mechatronics & Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074
  • Received:2017-06-30 Revised:2017-08-24 Published:2019-12-12

摘要: 针对复合电源混合动力系统能量管理的复杂多样性,以蓄电池和超级电容的荷电状态为状态变量,以发动机、电机的转矩分配比和蓄电池、超级电容的功率分配比为控制变量,将发动机燃油经济性和排放性作为目标函数,提出基于动态规划算法(Dynamic Programming)的复合电源混合动力系统控制策略,从结果中提取发动机、电机、蓄电池和超级电容工作规律,对发动机、电机的规则控制和蓄电池、超级电容的模糊控制进行优化。结果表明:通过DP算法优化后的系统发动机燃油经济性和排放性比原系统分别提高了13%和27.74%,说明优化后的控制的正确性和有效性。

关键词: 混合动力, 复合电源, 能量管理, 控制优化, 仿真

Abstract: Aiming at the complexity and diversity of energy management for HEV(hybrid electric vehicle) with hybrid power, taking the State of Charge of power battery and ultracapacitor as state variable, the torque distribution ratio between engine and motor, and the power distribution ratio between power battery and ultracapacitor as control variables, the fuel economy and emission performance as objective function, the Dynamic Programming (DP) control strategy for HEV with hybrid power is proposed. The working rules of the engine, motor, battery and ultracapacitor, are extracted from the results, and the rules of engine and motor and the fuzzy control of storage battery and supercapacitor are optimized. The simulation results showed that the fuel economy performance and emission performance is increased by 13% and 27.74% respectively compared with the original control strategy, which proves the accuracy and validity of proposed control strategy for HEV with hybrid power.

Key words: HEV, hybrid power, energy management, control optimization, simulation

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