This article presents a novel two-phase dual-model approach to more appropriately handle battery power constraint in model predictive control (MPC)-based hybrid electric vehicle (HEV) energy management. The approach fuses two typical battery models with different fidelities in two consecutive phases of MPC’s prediction horizon, where more accurate first-order resistor–capacitor ( RC ) model is adopted in the near phase, while a purely ohmic impedance model is used in the more distant phase. More importantly, it is mathematically rigorously proved that the first-order RC model can be reformulated as an equivalent single-state model while preserving accurate voltage calculation, provided that the length of phase one is properly determined. Finally, the formulated optimal energy management problem is solved using a forward dynamic programming (FDP) algorithm. Both numerical examination and model in the loop (MiL) test using an experimentally validated forward-facing simulator demonstrate that compared with traditional approach, the proposed approach can achieve better optimality in the condition of very sparse state discretization, which is imperative for real-time implementation.

中文翻译：

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基于 MPC 的 HEV 能量管理中增强的电池功率约束处理：一种两阶段双模型方法

本文提出了一种新颖的两相双模型方法，以更恰当地处理基于模型预测控制 (MPC) 的混合动力电动汽车 (HEV) 能量管理中的电池功率约束。该方法在 MPC 预测范围的两个连续阶段融合了两个具有不同保真度的典型电池模型，其中更准确的一阶电阻器 - 电容器（ 近相采用RC)模型，远相采用纯欧姆阻抗模型。更重要的是，在数学上严格证明了一阶RC 模型可以重新表述为等效的单态模型，同时保持精确的电压计算，前提是正确确定第一相的长度。最后，使用前向动态规划 (FDP) 算法解决制定的最优能源管理问题。使用经过实验验证的前向模拟器进行的数值检验和模型在环 (MiL) 测试表明，与传统方法相比，所提出的方法可以在非常稀疏的状态离散化条件下实现更好的优化，这对于实时性来说是必不可少的执行。