Abstract This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.

中文翻译：

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用于协调燃油经济性和电池退化的插电式混合动力电动汽车的实时预测能量管理

摘要 本文提出了一种用于协调控制燃油经济性和电池寿命的插电式混合动力汽车的实时预测能量管理策略 (PEMS)，包括速度预测器、充电状态 (SOC) 参考发生器和在线优化。 . 在速度预测器中，采用径向基函数神经网络算法来准确估计未来的驱动速度。基于预测速度和当前行驶距离，参考生成器可以在线确定预测范围内的SOC参考。为了协调燃料消耗和电池退化，建立了包括燃料消耗成本、电池充放电电成本和电池退化等效成本在内的成本最小化模型预测控制问题。为了减轻优化中的巨大计算负担，委托延续/广义最小残差（C/GMRES）算法实时寻找预期的发动机功率命令。由于原有的C/GMRES算法不能直接处理不等式约束，因此采用外部惩罚法来满足动力系统的物理不等式限制。进行了数值模拟，并在燃料消耗最小化和电池老化限制方面产生了所提出的 PEMS 的理想性能。更重要的是，与序列二次规划和遗传算法相比，所提出的C/GMRES算法在PEMS中显示出很好的求解质量和实时适用性。由于原有的C/GMRES算法不能直接处理不等式约束，因此采用外部惩罚法来满足动力系统的物理不等式限制。进行了数值模拟，并在燃料消耗最小化和电池老化限制方面产生了所提出的 PEMS 的理想性能。更重要的是，与序列二次规划和遗传算法相比，所提出的C/GMRES算法在PEMS中显示出很好的求解质量和实时适用性。由于原有的C/GMRES算法不能直接处理不等式约束，因此采用外部惩罚法来满足动力系统的物理不等式限制。进行了数值模拟，并在燃料消耗最小化和电池老化限制方面产生了所提出的 PEMS 的理想性能。更重要的是，与序列二次规划和遗传算法相比，所提出的C/GMRES算法在PEMS中显示出很好的求解质量和实时适用性。