To realize the efficient use of battery residual energy, this paper attempts to estimate both the state of energy (SoE) and the state of available power (SoAP) for li-ion battery packs. First, the parameters of a 1st-order equivalent circuit model are identified online where the charging and discharging resistances are separately modeled. Then a state of energy estimator, considering the energy dissipation by heat convection, is designed using an unscented particle filter. Afterwards, multiple constraints in terms of cut-off voltages, recommended residual energy, extreme currents, and powers are incorporated to aid in SoAP prediction. Experiments on a 4-cell battery pack using a high-dynamic load profile show that the SoE estimator is reliable against various working conditions. The predicted SoAP with different time horizons and at different temperatures can avoid the conflicts with the preset constraints while giving reliable predictions.

为了实现电池剩余能量的有效利用，本文尝试估计锂离子电池组的能量状态（SoE）和可用功率状态（SoAP）。首先，在线识别一阶等效电路模型的参数，其中充电和放电电阻分别建模。然后，考虑到热对流的能量耗散，使用无味粒子滤波器设计了一个状态能量估计器。之后，结合截止电压、推荐剩余能量、极端电流和功率方面的多个约束，以帮助进行 SOAP 预测。使用高动态负载曲线对 4 芯电池组进行的实验表明，SoE 估计器在各种工作条件下都是可靠的。