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Pareto Front Analysis of the Objective Function in Model Predictive Control Based Power Management System of a Plug-in Hybrid Electric Vehicle

机译:插电式混合动力电动车型模型预测控制电力管理系统的目标函数的帕累托正面分析

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Model predictive control strategy (MPC) has been identified as an efficient path for reducing fuel consumption, greenhouse gasses (GHG) emission, or degradation of power-train components for electrified vehicles. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. As the main contribution, this paper provides a Pareto-front analysis of the objective function taking into account the equivalent fuel consumption and the battery aging when the PHEV is in the charge sustaining (CS) mode. The results show that the MPC controller can decrease the battery capacity fade by 45% for only increasing the equivalent vehicle fuel consumption of 0.1% compared to an engine on-off thermostat control strategy.
机译:模型预测控制策略(MPC)已被识别为降低燃料消耗,温室气体(GHG)发射或电动车辆动力传动部件的降低的有效路径。 MPC是一种基于优化的控制策略,其旨在通过预测其未来的行为来找到系统的最佳控制动作。作为主要贡献,本文提供了在PHEV处于电荷维持(CS)模式时的等效燃料消耗和电池老化的目标函数的静态正面分析。结果表明,与发动机接通恒温控制策略相比,MPC控制器可以降低45℃的电池容量淡出45℃的电池容量。

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