基于GA优化的纯电动汽车能量管理策略研究

Research on Energy Management Strategy of Pure Electric Vehicle Based on GA Optimization

摘要: 为了解决单纯使用经验得到的隶属度函数构建的模糊控制器无法使纯电动汽车的电能控制效率最大化这一问题，提出了一种基于遗传算法优化的模糊PID控制策略。首先，在AVL Cruise软件中搭建纯电动汽车整车模型，同时根据整车能量管理策略设计了模糊PID控制器。在此基础上，采用遗传算法进一步优化模糊PID控制器的隶属度函数并建立能量管理策略模型，通过MATLAB/Simulink和AVL Cruise软件进行联合仿真。结果表明，所提遗传算法优化的模糊PID控制策略提高了整车的能量利用率，能够有效改善蓄电池的放电状态，延长电池的使用寿命，在NEDC循环工况下，最大爬坡度提高了18.71%，整车百公里加速时间缩短了15.25%，百公里耗电量降低了13.78%，有效提高了整车的动力性和经济性。所设计的能量管理策略为新能源汽车发展提供参考。

Abstract: In order to solve the problem that fuzzy controllers constructed solely using membership functions obtained from experience cannot maximize the efficiency of electric vehicle energy control, a fuzzy PID control strategy based on genetic algorithm optimization is proposed. Firstly, a pure electric vehicle model was built in AVL Cruise software, and a fuzzy PID controller was designed based on the vehicle energy management strategy. On this basis, genetic algorithm is used to further opti-mize the membership function of the fuzzy PID controller and establish an energy management strategy model. Joint simulation is conducted using MATLAB/Simulink and AVL Cruise software. The results show that the proposed genetic algorithm optimized fuzzy PID control strategy achieves the best energy utilization of the entire vehicle, effectively improving the discharge state of the battery, prolonging the service life of the battery. Under NEDC cycle conditions, the maximum climbing slope is increased by 18.71%, the acceleration time per 100 kilometers is shortened by 15.25%, and the power consumption per 100 kilometers is reduced by 13.78%, effectively improv-ing the power and economy of the entire vehicle. The designed energy management strategy pro-vides a reference for the development of new energy vehicles.