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Improved EKF for SOC of the storage battery

机译:改进的EKF用于蓄电池的SOC

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摘要

Aiming at the electric automobile in the running state of the complicated working condition, an innovative battery SOC estimation method is presented. Based on a new type of on-line measurement in storage battery parameters, improved EKF algorithm is used to estimate the remaining battery capacity. By isolating single cells and acquainting parameters, the unit cell's SOC is estimated through the Kalman algorithm, and we can calculate assembled battery SOC by integrating unit cell's SOC. This algorithm overcomes the changes of electric vehicle battery parameters which are complicated and the traditional estimation algorithm has defects of low accuracy of SOC. The technology put forward in this paper overcomes the flaw. And the internal resistance of the battery can be estimated. The research has an important significance on SOH. Analysis of the test shows that, using this method for on-line estimation of battery SOC, the estimation accuracy is relatively high can reflect the real residual capacity of battery better
机译:针对电动汽车在复杂工况下的运行状态,提出了一种创新的电池SOC估算方法。基于一种新型的蓄电池参数在线测量,改进的EKF算法用于估算剩余电池容量。通过隔离单电池并了解参数,可通过卡尔曼算法估算单电池的SOC,并且可以通过合并单电池的SOC来计算组合电池的SOC。该算法克服了电动汽车电池参数变化复杂的问题,传统的估计算法具有SOC精度低的缺点。本文提出的技术克服了这一缺陷。并且可以估计电池的内阻。该研究对SOH具有重要意义。测试分析表明,采用该方法进行电池SOC在线估算,估算精度较高,可以较好地反映电池的实际剩余容量。

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