多模态多目标优化算法的混合动力系统能量管理控制OA

To optimize the energy management for parallel hybrid electric vehicles,this paper proposes a multimodal multi-objective particle swarm optimization algorithm based on a special crowding distance(SCD),named MO-Ring-PSO-SCD.It simultaneously optimizes the energy control strategy and powertrain parameters,with fuel economy,ride comfort,and dynamic performance as the optimization objectives despite the constraints such as battery state-of-charge balance.Results demonstrate the algorithm outperforms the conventional MPSO,improving fuel economy by 13.46%,ride comfort by 11.71%,and dynamic performance by 12.74%.Compared with MPSO,it further enhances fuel economy and ride comfort by 1.16%and 0.63%while dynamic performance decreases slightly by 3.03%.Nevertheless,the obtained Pareto solution set exhibits superior distribution breadth,quantity,and uniformity.By enhancing solution diversity and incorporating multimodal characteristics,the algorithm may provide more comprehensive trade-off options for energy management strategies,markedly improving the optimizability of the decision space.