• CN:11-2187/TH
  • ISSN:0577-6686

›› 2009, Vol. 45 ›› Issue (2): 56-61.

• 论文 • 上一篇    下一篇

燃料电池混合动力参数辨识及整车控制策略优化

徐梁飞;李相俊;华剑锋;李建秋;欧阳明高   

  1. 清华大学汽车安全与节能国家重点实验室
  • 发布日期:2009-02-15

Parameter Identification and Control Strategy Optimization of Hybrid Fuel Cell Powertrain

XU Liangfei;LI Xiangjun;HUA Jianfeng;LI Jianqiu;OUYANG Minggao   

  1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University
  • Published:2009-02-15

摘要: 为优化燃料电池混合动力系统经济性及耐久性,建立了模型参数辨识及整车控制策略优化方法。首先给出了燃料电池混合动力系统氢气消耗及蓄电池模型参数的最小二乘离线辨识算法。台架试验验证了其有效性。其次给出了整车控制策略,包括电动机控制策略和能量管理策略两部分。电动机运行状态根据挡位信号和司机踏板信号可以划分为两类:制动状态和非制动状态。电动机目标转矩在这两类状态中采取不一样的计算方法。能量管理策略包括稳态分配和动态补偿两个模块。再次,采用基于多目标的遗传优化算法对整车控制策略相关参数进行优化,优化目标为混合动力系统在“中国城市公交典型工况”中等效氢气消耗量最小。工况测试结果表明,使用优化算法后系统经济性从9.6 kg/100km提升到7.6 kg/100km,减小了燃料电池输出功率的波动并维持蓄电池SOC在平衡点附近。

关键词: 多目标遗传优化算法, 能量管理, 燃料电池混合动力, 整车控制, 最小二乘辨识

Abstract: In order to improve fuel economy and durability of hybrid fuel cell electric vehicle, a parameter identification algorithm and an optimization method for vehicle control strategy are developed. Parameters of hydrogen consumption and battery model are identified on the basis of least square method. The vehicle control strategy includes two parts, the motor control and energy management strategies. According to shift and pedal signals, motor operation can be classified into brake and non-brake modes. Motor target torque is calculated differently in the two modes. There are two blocks in energy management strategy, the stable distribution and dynamic compensation algorithms. All the parameters in the vehicle control strategy are optimized using a multi-target genetic optimization algorithm so that the equivalent hydrogen consumption in "China city bus typical cycle" is minimized. Experiment results show that, the fuel economy is improved from 9.6 kg/100km to 7.6 kg/100km, power fluctuation of fuel cell stack is decreased and battery SOC is kept nearby the balance point.

Key words: Energy management, Hybrid fuel cell powertrain, Least square identification, Multi-target genetic optimization algorithm, Vehicle control

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