The battery power capability is intimately correlated with the climbing, braking and accelerating performance of the electric vehicles. Accurate power capability prediction can not only guarantee the safety but also regulate driving behavior and optimize battery energy usage. However, the nonlinearity of the battery model is very complex especially for the lithium iron phosphate batteries. Besides, the hysteresis loop in the open-circuit voltage curve is easy to cause large error in model prediction. In this work, a multi-parameter constraints dynamic estimation method is proposed to predict the battery continuous period power capability. A high-fidelity battery model which considers the battery polarization and hysteresis phenomenon is presented to approximate the high nonlinearity of the lithium iron phosphate battery. Explicit analyses of power capability with multiple constraints are elaborated, specifically the state-of-energy is considered in power capability assessment. Furthermore, to solve the problem of nonlinear system state estimation, and suppress noise interference, the UKF based state observer is employed for power capability prediction. The performance of the proposed methodology is demonstrated by experiments under different dynamic characterization schedules. The charge and discharge power capabilities of the lithium iron phosphate batteries are quantitatively assessed under different time scales and temperatures.

电池的动力能力与电动汽车的爬坡，制动和加速性能密切相关。准确的功率容量预测不仅可以保证安全性，还可以调节驾驶行为并优化电池能量使用。但是，电池模型的非线性非常复杂，特别是对于磷酸锂铁电池。此外，开路电压曲线中的磁滞回线很容易在模型预测中引起较大的误差。在这项工作中，提出了一种多参数约束动态估计方法来预测电池连续周期的功率容量。提出了考虑电池极化和滞后现象的高保真电池模型，以近似磷酸铁锂电池的高非线性。详细阐述了具有多个约束条件的功率能力的分析，尤其是在功率能力评估中考虑了能量状态。此外，为了解决非线性系统状态估计的问题并抑制噪声干扰，将基于UKF的状态观测器用于功率能力预测。通过在不同的动态表征时间表下进行的实验证明了所提出方法的性能。磷酸铁锂电池的充电和放电功率容量是在不同的时间范围和温度下进行定量评估的。基于UKF的状态观察器用于功率能力预测。通过在不同的动态表征时间表下进行的实验证明了所提出方法的性能。磷酸铁锂电池的充电和放电功率容量是在不同的时间范围和温度下进行定量评估的。基于UKF的状态观察器用于功率能力预测。通过在不同的动态表征时间表下进行的实验证明了所提出方法的性能。磷酸铁锂电池的充电和放电功率容量是在不同的时间范围和温度下进行定量评估的。