基于混沌遗传算法的PHEV能量管理策略优化

提出了一种新的混沌遗传算法,改进了混沌映射和遗传算法的结合方式,使种群在进化的过程中能够混沌搜索解空间,从而增强遗传算法的遍历性.该算法有效地克服了遗传算法局部收敛的缺陷.在软件ADVISOR2002中,以一辆采用模糊能量管理策略的插电式混合动力电动汽车(Plug-in Hybrid Electrical Vehicle,PHEV)为研究对象,应用该混沌遗传算法对其隶属函数和控制规则进行了优化.仿真结果表明,该算法可以实现对模糊控制器的全局优化.与原模糊控制策略相比,优化后的燃油经济性提高了5.15%,CO排放减少了6.39%.

Optimization of Energy Management Strategy of PHEV Based on Chaos-Genetic Algorithm

This paper proposed a new Chaos-Genetic Algorithm, which improved the combination between the chaos mapping and genetic algorithm in order to add chaotic search to the population evolution, so that the ergodicity of the genetic algorithm can be enhanced. And the algorithm can effectively overcome the defects of local optimum of the genetic. In the software ADVISOR2002, for a Plug-in Hybrid Electrical Vehicle (PHEV) using fuzzy energy management strategy as the research object, the membership functions and rules of fuzzy EMS were optimized with Chaos-Genetic Algorithm. Simulation results demonstrate that this Chaos-Genetic Algorithm can optimize the fuzzy EMS globally. Compared with the original fuzzy control strategy, the optimized fuel economy increases by 5.15% and the CO emission reduces by 6.39%.