Research on optimization of control strategy for hybrid energy storage system (HESS) of the electric vehicle (EV), a new adaptive control strategy based on particle swarm optimization (PSO) algorithm is proposed in this paper. The steady-state power of the filtered power is used as the ideal output power of the battery. For the steady-state current output of the battery, the output power of the ultracapacitor is dynamically adjusted by the proportional-integral-derivative (PID) controller to construct a power difference control structure. The parameters of PID controller are optimized by PSO algorithm, and the target test is compared and analyzed based on MATLAB/Advisor. The research results show that the proposed PSO-PID control strategy can quickly eliminate the power deviation and achieve the approximate global optimization of the EV energy management strategy. Compared with the pre-optimized PID control strategy, the output current and power of the battery pack are smoother and the total power consumption is reduced by 3.8360% and 0.5125%, respectively. Then, the energy consumption parameters of PSO-PID are compared with the theoretical minimum energy consumption calculated by dynamic programming (DP) algorithm, and the deviation is less than 1% under both conditions.

针对电动汽车混合动力储能系统（HESS）控制策略的优化研究，提出了一种基于粒子群算法（PSO）的自适应控制策略。滤波后的功率的稳态功率用作电池的理想输出功率。对于电池的稳态电流输出，超级电容器的输出功率由比例积分微分（PID）控制器动态调节，以构建功率差控制结构。通过PSO算法对PID控制器的参数进行优化，并基于MATLAB / Advisor对目标测试进行比较和分析。研究结果表明，提出的PSO-PID控制策略可以快速消除功率偏差，实现电动汽车能源管理策略的近似全局优化。与预先优化的PID控制策略相比，电池组的输出电流和功率更平滑，总功耗分别降低了3.8360％和0.5125％。然后，将PSO-PID的能耗参数与动态规划（DP）算法计算出的理论最小能耗进行比较，两种情况下的偏差均小于1％。