基于遗传算法的HEV控制策略优化
Optimizing HEV Control Strategy Based on Genetic Algorithm

混合动力汽车控制策略参数的选取对系统性能有重大的影响。为此,建立了以动力性能为约束指标、以燃油消耗率和排放为评价指标的混合动力汽车控制策略参数优化模型,并对控制策略参数进行优化。利用加权系数法整合评价指标,结合遗传算法工具箱GATBX对控制策略参数进行多目标优化,并利用ADVISOR对优化前后的性能进行仿真。仿真结果表明:在保证动力性的前提下有效降低了混合动力汽车燃料消耗量和排放,其中油耗和NOx降低了17%,HC和CO降低了大约7%。
The selection of parameters for a hybrid electric vehicle (HEV) control strategy has a significant influence on its performance. An optimization model for the HEV control strategy is set up to optimize the parameters of the HEV control strategy, including a constraint indicator of power performance and an evaluation indicator of fuel consumption and emissions. Since the parameter optimization for the HEV control strategy is a multi-objective optimization problem, evaluation indicators are integrated into one objective function by weighting their coefficients. GATBX (Genetic Algorithms Tool Box) was used to optimize the parameters for the HEV control strategy, and the performance of the optimized HEV was simulated by ADVISOR. The simulation results and their analysis show that the optimized control strategy can effectively reduce fuel consumption and emissions without sacrificing power performance and fuel consumption and that NOx emission is reduced by 17% and that HC and CO emissions are reduced by about 7%