• CN:11-2187/TH
  • ISSN:0577-6686

›› 2012, Vol. 48 ›› Issue (14): 91-96.

• 论文 • 上一篇    下一篇

基于随机动态规划的混合动力履带车辆能量管理策略

邹渊;陈锐;侯仕杰;胡晓松   

  1. 北京理工大学电动车辆国家工程实验室
  • 发布日期:2012-07-20

Energy Management Strategy for Hybrid Electric Tracked Vehicle Based on Stochastic Dynamic Programming

ZOU Yuan;CHEN Rui;HOU Shijie;HU Xiaosong   

  1. National Engineering Lab for Electric Vehicles, Beijing Institute of Technology
  • Published:2012-07-20

摘要: 混合动力履带车辆采用发动机—发电机组和电池组混合供电,必须设计满足车辆动力性和燃油经济性约束的能量管理策略。针对串联式混合动力履带车辆,提出一种基于随机动态规划的能量管理策略设计方法。以实车行驶试验数据为目标工况,将驾驶员功率需求抽象为随车速变化的马尔科夫过程。建立发动机—发电机组、电池组以及直流母线功率平衡动态模型。以目标工况中燃油消耗及电池最终荷电状态的偏差作为车辆的优化控制成本函数,建立车辆能量管理最优控制问题。采用策略迭代法求解以发动机转速、电池组荷电状态、车速和驾驶员功率需求为输入、发动机电子节气门为输出的最优控制策略。所得控制策略通过基于前向车辆模型的仿真以及行驶试验验证。结果表明,相对于原发动机多点控制策略,所得最优控制在满足目标工况同时,燃油经济性明显提高。

关键词: 混合动力, 履带车辆, 马尔科夫链, 能量管理策略, 随机动态规划

Abstract: Energy management strategy is indispensable and should be designed with the constraints from vehicles drivability and economy for the hybrid electric tracked vehicle with the engine-generator set and the power battery pack simultaneously. An energy management strategy based on stochastic dynamic programming is proposed for a serial hybrid electric tracked vehicle. Based on the driving schedule from the field test data, the probability of power demand is calculated by maximum likelihood method, the power demand for driver is modeled as a Markov chain process which varies as the vehicle speed changes. The engine-generator set, battery and power balance between them is modeled. The fuel consumption and the deviation between final and initial state of charge of battery were adopted as the cost value function of the optimal control model. A policy iteration algorithm method is applied to solve the optimal control problem and the optimal control will be determined by the state feedbacks including the engine speed and the battery state of charge, the vehicle speed and the power request. The control strategy is verified through the feed-forward simulation model and field test. A considerable improvement in fuel economy, compared to the former rule-based control strategy, is observed.

Key words: Energy management, Hybrid powertrain, Markov chain, Stochastic dynamic programming, Tracked vehicle

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