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• W4310453921 abstract "The present study investigates an energy management strategy based on reinforcement learning for series-parallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller." @default.
• W4310453921 created "2022-12-10" @default.
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• W4310453921 date "2023-01-01" @default.
• W4310453921 modified "2023-10-18" @default.
• W4310453921 title "Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life" @default.
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• W4310453921 doi "https://doi.org/10.1016/j.egyai.2022.100217" @default.
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