为提高并联式混合动力汽车的燃油经济性和降低其废气排放量,采用基于自适应混沌粒子群优化算法建立并联式混合动力汽车控制优化模型,对其逻辑门限控制参数进行优化,并与PSO算法和GA算法的优化结果进行比较;利用ADVISOR仿真软件对其优化参数进行仿真验证,并对其逻辑门限控制参数优化前的仿真结果与自适应混沌粒子群优化后的仿真结果进行对比.研究结果表明:自适应混沌粒子群优化算法具有较快的收敛速度和较高的收敛精度,能有效避免早熟收敛问题;100km油耗至少可降低12％,HC排放量可降低6％,CO排放量可降低5％,NOx排放量可降低8％.%In order to reduce the fuel consumption and emissions of the parallel hybrid electric vehicle, a new optimization model of the logic threshold control parameter was established based on adaptive chaos particle swarm algorithm. The model was verified by comparing the optimized results of ACPSO algorithm with PSO and those of GA algorithm. Based on ADVISOR, the optimized fuel consumption and emissions were compared with those which were not optimized. The results show that the ACPSO not only has great advantages of convergence property, but also avoids being trapped in local optimum. There is at least 12% reduction in the fuel consumption per 100 km and 6%, 5% and 8% decrease in the discharge of HC, CO and NOX respectively in UDDC working conditions.
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