Multi-layer parameter scanning (MLPS) algorithm and simulated annealing particle swarm optimization (SAPSO) algorithm are used respectively to optimize the parameters of logic threshold control strategy for parallel hybrid electric vehicle (PHEV). The PHEV with optimized parameters is simulated with TEST-CITY-HWY test procedure, and the results are compared with that before optimization. The results indicate that after optimization with MLPS algorithm, the fuel consumption and the emissions of HC and N0x reduce by 11. 98% , 6.01% and 4. 03% respectively, but with an increase of 25. 18% in CO emission; while the optimization with SAPSO algorithm leads to an all-round reduction in fuel consumption and the emissions of HC, CO and NOx respectively of 13. 61% , 9. 57% , 27. 78% and 18. 53%. In addition, SAPSO optimization also results in a slight increase of battery SOC compared with MLPS, showing the superiority of SAPSO over MLPS in respect of the effects of control parameter optimization for PHEV.%分别采用多层次参数扫描(MLPS)算法和模拟退火粒子群优化(SAPSO)算法对并联式混合动力车逻辑门限控制策略的参数进行优化.将优化后的车辆以TEST-CITY-HWY测试循环进行仿真,并将结果与优化前的车辆的仿真结果进行对比.结果表明,经MLPS算法优化后,燃油消耗和HC与NOx排放分别下降了11.98％、6.01％和4.03％,但CO排放增加了25.18％；经SAPSO算法优化后,燃油消耗和HC、CO与NOx排放分别下降了13.61％、9.57％、27.78％和18.53％,且电池荷电状态(SOC)比MLPS优化略高.说明SAPSO算法在混合动力车控制参数优化效果上明显优于MLPS算法.
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