运用改进蜉蝣算法的增程式电动汽车能量管理策略研究

Research on Energy Management Strategy of Extended-range Electric Vehicle Using Improved Mayfly Algorithm

针对增程式电动汽车基于规则的能量控制策略优化问题,提出了基于改进的蜉蝣优化算法的多点能量管理控制策略。以增程式轻型卡车为研究对象,建立整车多目标优化的数学模型,采用改进的蜉蝣优化算法进行离线寻优。将整车需求功率和电池SOC值的变化量作为实时输入参数来设计模糊控制器,对APU当前工作点的最小保持时间进行控制。最后在WLTP测试工况下,采用MATLAB/Simulink和Cruise联合仿真对优化的能量管理控制策略进行了分析与验证。研究结果表明,改进的优化算法IMA继承了MA的优点,且提高了算法在寻优前期的全局搜索能力和整体的收敛速度。所提出的多点能量管理控制策略与恒温器、功率跟随型能量管理控制策略相比,综合性能分别提高了16.2%和7.8%,有效提高了整车综合性能。

Aiming at the rule-based energy control strategy optimization for the extended-range electric vehicles,a multi-point energy management control strategy based on the improved mayfly optimization algorithm is proposed.Taking the extended-range light truck as the object,the model for multi-objective optimization of the whole vehicle is established,and the improved mayfly optimization algorithm is used for offline optimization.A fuzzy controller is designed by taking the vehicle demand power and the variation of the battery SOC value as real-time input parameters to control the minimum hold time of the APU's current operating point.Finally,under the WLTP test condition,the optimized energy management control strategy is analyzed and verified by using MATLAB/Simulink and Cruise co-simulation.The results show that the improved optimization algorithm IMA inherits the advantages of MA,and improves the global search ability and the overall convergence speed of the algorithm in the early stage of optimization.Comparing with the thermostat and power-following energy management control strategies,the present multi-point energy management control strategy improves the comprehensive performance by 16.2%and 7.8%respectively,which effectively improves the overall performance of the vehicle.