应用粒子群算法优化模糊规则的自适应多目标控制策略

Adaptive multi-objective control strategy based on particle swarm optimization algorithm optimized fuzzy rules

中文关键词  插电式混合动力汽车  多目标优化控制策略  模糊控制  粒子群算法  等效燃油消耗最小策略

英文关键词  plug-in hybrid electric vehicle  multi-objective optimization control strategy  fuzzy control  particle swarm optimization  equivalent consumption minimization strategy

中文摘要

      为了改善一款插电式混合动力汽车(PHEV)的燃油经济性和排放性能, 本文利用等效燃油消耗最小方法 (ECMS)建立以油电转换等效因子为核心的多目标控制策略. 首先, 在建立PHEV模型和多目标优化价值函数的基础 上, 构建了模糊比例积分等效因子优化模型. 随后, 利用粒子群优化(PSO)算法通过对隶属度函数值及控制规则进 行优化以得到更为准确的比例和积分系数, 进而获得更为精确的等效因子用以合理分配动力部件的能量, 从而建立 了基于PSO–fuzzy的PHEV等效因子自适应多目标优化控制策略. 最后以福州市实际行驶工况数据对所提出的控制 策略进行仿真研究, 结果表明在该策略控制下PHEV的燃油经济性和排放性均得到了明显改善. 其中, 基于PSO– fuzzy的控制策略与基于一般模糊控制的策略相比, 等效油耗降低了9.0%, HC排放量降低了2.7%, CO排放量降低 了2.9%, NOx的排放量降低了7.8%.

英文摘要

      To improve the fuel consumption and emission performance of a plug-in hybrid electric vehicle (PHEV), this paper builds a multi-objective control strategy using equivalent consumption minimization strategy which the core is determining the equivalence factor. Firstly, based on the established PHEV model and cost function of multi-objective optimization, an equivalent factor optimization model combining the fuzzy proportional integral controller is constructed. Then, particle swarm optimization (PSO) algorithm is adopted to get more accurate proportion and integral coefficient by optimizing membership function and control rules and then obtains more accurate equivalent factor to distribute the power between different power components reasonably. Therefore, the PSO–fuzzy based multi-objective optimization control strategy for PHEV with equivalent factor adaption is designed. Finally, simulation analysis and research on the proposed control strategy are carried out based on the actual driving cycle of Fuzhou city. Results show that the proposed control strategy can improve the fuel economy and emission performance of the PHEV significantly. Compared with the general fuzzy control strategy, the PSO–fuzzy based control strategy can reduce the equivalent fuel consumption by 9.0%, HC emissions by 2.7%, CO emissions by 2.9% and NOx emissions by 7.8%, respectively.