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Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle based on Genetic Algorithm

机译:基于遗传算法的平行混合动力电动汽车优化模糊逻辑控制策略

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For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.
机译:对于PHEV能源管理,在本文中,作者提出了EMS,基于模糊逻辑控制策略的优化。由于FLC和模糊规则基础的成员函数通过专家的经验或通过设计人员通过实验分析获得,因此他们无法使FLC获得优化结果。因此,作者使用遗传算法优化FLC的隶属函数,进一步提高车辆性能。最后,通过使用电动车软件顾问模拟和分析,结果表明,该策略可以轻松控制发动机和电机,确保电池充电和放电之间的平衡,与电气辅助控制策略相比,燃料消耗和废气排放相比也已降至低于43.84%。

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