• CN:11-2187/TH
  • ISSN:0577-6686

›› 2004, Vol. 40 ›› Issue (8): 127-133.

• 论文 • 上一篇    下一篇

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混合动力汽车能量管理策略的四步骤设计方法

朱元;田光宇;陈全世;吴昊   

  1. 清华大学汽车安全与节能国家重点实验室
  • 发布日期:2004-08-15

FOUR-STEP METHOD TO DESIGN THE ENERGY MANAGEMENT STRATEGY FOR HYBRID VEHICLES

Zhu Yuan;Tian Guangyu;Chen Quanshi;Wu Hao   

  1. State Key Laboratory of Automobile Safety and Energy, Tsinghua University
  • Published:2004-08-15

摘要: 以行星齿轮结构的混合动力汽车的动力系统为研究对象,提出了一套能量管理策略的四步骤设计方法。第一步,引入混杂动态系统理论来描述既有离散事件动态系统特征又有连续变量动态系统特征的混合动力系统;第二步,利用序列二次项规划算法和动态规划算法分别求解出最佳功率分配和最佳工作模式切换序列的基本规律;第三步,建立基于规则和模糊规则的能量管理策略;第四步,利用遗传算法对第三步已经建立的工作模式切换规则和功率分配的模糊控制规则中的参数进行优化,以获得更佳的控制效果。

关键词: 动态规划, 混合动力汽车, 混杂动态系统, 能量管理策略, 遗传算法

Abstract: Hybrid vehicles have two or more sources of on-board power. A power control strategy, referred to as the “energy management strategy”, is needed to coordinate all the components of powertrain so that they can work together in an optimal manner (optimal vehicle operating mode and optimal power distribution) to achieve multiple design objectives, such as high fuel economy and low emissions. An innovative four-step design method is presented and an optimal energy management strategy for a power split powertrain hybrid vehicle is designed. In the first step, a hybrid dynamical system theory is introduced to formulate the hybrid vehicle control system that incorporates both continuous and discrete dynamics. In the second step, the sequential quadratic programming (SQP) method is applied to optimize power distribution. The dynamic programming method is employed to solve the complex vehicle operating mode transition problem. In the third step, a rule-based system and a fuzzy rule system are developed based on the statistical numerical solutions derived from the second step. In the fourth step, a genetic algorithm is applied to the simultaneous optimization of parameters of membership functions, weights of the rules and rule sets for fuzzy rule system and parameters of the rule-based system.

Key words: Dynamic programming, Energy management strategy, Genetic algorithm, Hybrid dynamical system, Hybrid vehicles

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