基于多目标优化的燃料电池汽车能量管理策略

Energy management strategy of fuel cell vehicle based on multi-objective optimization

为了提高燃料电池混合动力汽车(FCHEV)的燃料经济性并优化燃料电池的耐久性,提出了一种燃料电池混合动力汽车能量管理策略(EMS)的多目标优化方法。根据FCHEV混合动力系统的功率流和关键部件的效率特性,提出了驱动系统的等效氢气消耗模型。此外,还考虑了负载变化对燃料电池寿命的影响。提出了一种智能功率分配方法来实现能量管理,即基于模糊逻辑控制(FLC)的控制策略。在进一步的研究中,为了改进提出的能量管理策略,采用遗传算法对模糊控制器的参数进行优化了。提出了以等效氢气消耗量和燃料电池寿命为优化目标的多目标优化问题,并采用改进的快速非支配排序遗传算法(NSGA-II)求解多目标优化问题,优化控制参数。最后,对上述算法的优化结果进行了分析,并利用高级车辆仿真平台ADVISOR对优化策略和其他策略进行了典型工况下的仿真和比较。结果表明,与未优化的FLC策略相比,等效氢耗普遍降低了5.8%左右,燃料电池的寿命衰减普遍降低约59%,验证了优化后的控制策略具有一定的优越性。

A multi-objective optimization method for energy management strategy(EMS)of the fuel cell hybrid electric vehicle(FCHEV)is proposed to improve the efficiency of the drive system and optimize the durability of fuel cell.The equivalent hydrogen consumption model of the hybrid power system is established according to the power flow and the efficiency characteristics of key components.In addition,the lifetime degradation of fuel cell based on the load variation is considered.The energy management system is achieved by presenting an intelligent power allocation method,that is,the control strategy based on fuzzy logic control(FLC).In further research,in order to ameliorate the energy management strategy,the parameters of the fuzzy controller are optimized with the assistance of genetic algorithm(GA).A multi-objective optimization problem which takes equivalent fuel consumption and fuel cell lifetime as optimization targets is proposed.The improved fast non-dominated sorting genetic algorithm(NSGA-II)is used to solve the multi-objective optimization problem,so as to optimize the control parameters.Finally,the optimization results of the above algorithm are tested,and the optimized strategy and other strategies are simulated by the advanced vehicle simulator(ADVISOR)under typical conditions.The results show that,compared with the non optimized FLC strategy,the equivalent hydrogen consumption is generally reduced by about 5.8%,and the life decay of the fuel cell is generally reduced by about 59%,which verifies the superiority of the optimized control strategy.