PHEV复合储能系统设计及基于MODA的参数优化

为了提高插电式混合动力汽车的燃油经济性、降低污染物的排放，并解决插电式混合动力汽车单一动力电池低比功率、无法响应暂态功率需求的问题，设计蓄电池和超级电容并联的复合储能系统，采用带有滑动窗口的实时小波功率分配策略，并对滑动窗口长度进行选择。该功率分配策略将复合储能系统的需求功率分解成高频和低频两部分，超级电容接收高频分量，蓄电池接收低频分量，避免了高频分量对于蓄电池的冲击，提高了蓄电池的耐久性和可靠性。制定基于规则的控制策略，以整车燃油消耗量和污染物排放量为优化目标，利用多目标蜻蜓算法对相关控制参数进行优化。基于ADVISOR搭建含有复合储能系统的插电式混合动力汽车整车仿真模型，采用新欧洲行驶循环工况进行测试，并通过与带精英策略的非支配排序遗传算法进行对比，验证算法的有效性。研究结果表明：利用多目标蜻蜓算法优化后的车辆百公里燃油消耗平均降低了12.71%，污染物综合排放性能平均下降了10.05%；相对于优化前，发动机输出功率减少，电机输出功率增加，发动机和电机的工作效率均得到了显著提升；Pareto最优解的收敛性和覆盖范围优于带精英策略的非支配排序遗传算法，同时得到的多组Pareto最优解为整车设计和优化提供了更多选择。

PHEV Composite Energy Storage System Design and Parameter Optimization Based on MODA

To improve the fuel economy of plug-in hybrid electric vehicle, reduce pollutant emissions, and solve the problem of low specific power of the single-power battery that unable to respond to transient power demand in plug-in hybrid, a hybrid energy storage system (HESS) with parallel connection between battery and super-capacitor was designed.In the HESS, a real-time wavelet power allocation strategy with a sliding window was adopt, and the length of the sliding window was selected. This power distribution strategy decomposed the required power of the HESS into high-frequency and low-frequency parts.The super-capacitor received high-frequency components and the battery received low-frequency components, so the impact of high-frequency components on the battery was avoided and the durability and reliability of the battery was improved. A rule-based control strategy was implemented,with vehicle fuel consumption and pollutant emissions as the optimization goals, and a multi-objective dragonfly algorithm (MODA) was used to optimize related control parameters.A plug-in hybrid electric vehicle with the HESS was developed based on ADVISOR software, and a simulation analysis was carried out in the New European Driving Cycle, the effectiveness of the algorithm was verified by comparison with the non-dominated sorting genetic algorithm with elite strategy (NSGA-Ⅱ). The research results show that the fuel consumption per 100 kilometers of the vehicle optimized by the MODA is reduced by 12.71% on average, and the comprehensive pollutant emission performance is reduced by 10.05% on average. Compared with the optimization before, the engine output power is reduced and the motor output power is increased, the working efficiency of the engine and the motor has been significantly improved. The convergence and coverage of the Pareto optimal solution is better than that of the NSGA-Ⅱ, and at the same time, the multiple Pareto optimal solution provides more choice space for vehicle design and optimization.