In this study, multi-objective optimum energy management strategies are developed for pre- and post-transmission parallel hybrid electric vehicles. The first strategy aims to improve fuel economy and electric system efficiency, while the second strategy adds the improvement in battery performance and life. Multi-objective genetic algorithm is adopted to solve the formulated optimum energy management problems, hence generates optimum control inputs for each drivetrain configuration. Mathematical models of vehicle dynamics and drivetrain components are integrated with the developed strategies in a simulation environment. Results for both strategies are compared to a baseline rule-based energy management strategy, commonly used as a benchmark in literature. Additionally, both drivetrain configurations are compared and analyzed through different standard driving cycles. The results show the significant improvement in battery performance due to its consideration in the energy management strategy, with either null or positive effect on fuel consumption. Additionally, the results of pre-transmission drivetrain configuration show noticeable improvement in battery performance as compared to the post-transmission results.

在这项研究中，为前后并联混合动力电动汽车开发了多目标优化能量管理策略。第一个策略旨在提高燃油经济性和电力系统效率，而第二个策略则增加了电池性能和寿命的改进。采用多目标遗传算法解决制定的最优能量管理问题，从而为每个传动系统配置生成最优控制输入。车辆动力学和传动系统组件的数学模型与仿真环境中开发的策略相集成。将这两种策略的结果与基于规则的基线能源管理策略进行了比较，该策略通常用作文献中的基准。此外，通过不同的标准驾驶循环对两种传动系统配置进行比较和分析。结果表明，由于在能源管理策略中考虑了电池性能，电池性能得到了显着改善，对燃料消耗产生了零影响或正影响。此外，与变速箱后的结果相比，变速箱前传动系统配置的结果显示电池性能有显着改善。