增程式电动汽车能量管理策略控制参数优化研究

Research on optimization of control parameters of energy management strategy for extended-range electric vehicles

为了进一步提高增程式电动汽车发动机的燃油经济性和排放性,对基本遗传算法进行改进,生成改进的自适应遗传算法,并将其应用于增程式电动汽车能量管理策略控制参数的优化。通过权值函数的方法将燃油消耗和排放多目标优化问题转化为单目标优化问题,并采用罚函数的方法对工况运行前后蓄电池荷电状态的增量进行约束,对增程式电动汽车发动机燃油经济性和排放性进行优化仿真。仿真结果表明,采用改进的自适应遗传算法能够有效优化增程式电动汽车能量管理系统的控制参数,使发动机的燃油经济性和排放性得到较大的提高。

In order to increase the vehicle fuel economy and improve the emission performances,a modification has been done with Single Genetic Algorithm(SGA)and an Improved Adaptive Genetic Algorithm(IAGA)is developed.The algorithm is then applied to the control parameters optimization of E-REV energy management strategy.Through the method of weight function,the Multi-objective optimization problem for E-REV fuel consumption and emission is changed into a single objective optimization problem.Further,the penalty function approach is used to limit the increment of battery SOC before and after the driving cycle.The simulation results show that the improved adaptive genetic algorithm can effectively optimize the control parameters of the extended-range electric vehicle energy management system,which greatly improves the fuel economy and emission performance of the engine.