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Power battery SOC estimation with combination method based on UKF and open circuit voltage

机译:基于UKF和开路电压组合方法的动力电池SOC估算。

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摘要

The state of charge (SOC) is an important parameter in the battery management system. The SOC is usually estimated with the battery voltage and other state. Owing to the initial value problem of SOC, the estimation error may increase greatly. In order to solve this problem, the combination method based on unscented Kalman filter (UKF) and open circuit voltage is proposed in this paper. First, the PNGV model is used, and its parameters are identified with HPPC test data. Then, the estimation process is proposed. In order to verify the method, the PNGV battery model is established in the Simulink Toolbox. With the battery model, the SOC combination estimation method was simulated and compared with Ah counting and extended Kalman filter (EKF) methods. The simulation results show that the combination method gives smaller estimation error and calculation amount, so the combination estimation method is promising and useful for estimating the battery SOC.
机译:充电状态(SOC)是电池管理系统中的重要参数。通常用电池电压和其他状态估算SOC。由于SOC的初值问题,估计误差可能会大大增加。为了解决这个问题,本文提出了一种基于无味卡尔曼滤波器(UKF)和开路电压的组合方法。首先,使用PNGV模型,并通过HPPC测试数据确定其参数。然后,提出了估计过程。为了验证该方法,在Simulink Toolbox中建立了PNGV电池模型。利用电池模型,模拟了SOC组合估计方法,并将其与Ah计数和扩展卡尔曼滤波器(EKF)方法进行了比较。仿真结果表明,该组合方法估计误差较小,计算量较小,因此该组合估计方法有望用于电池SOC的估计。

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