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锂电池荷电状态(SOC)的准确估算是制约电动汽车发展的关键技术之一。针对传统Kalman滤波算法因固定的噪声滤波初值不能够跟随工况变化致使SOC估算不准确的问题,基于PNGV模型建立状态空间方程组,将Sage-Husa自适应滤波算法融合到无迹卡尔曼滤波(UKF)算法之中,对噪声进行实时预测和修正,进而提高SOC的估算精度。仿真实验结果表明,AUKF比UKF的估算值更接近于理论参考值,AUKF解决了UKF因固定噪声带来的误差问题,可提高电动汽车启动、巡航、制动等复杂工况下的电池组电流剧烈变化中SOC的估算精度。
Lithium battery state of charge (SOC) accurate estimation is one of the key technologies restricting the development of electric vehicles. Aiming at the problem that the traditional Kalman filter algorithm can not estimate the SOC accurately due to the change of working conditions due to the fixed initial value of noise filter, the state space equations are established based on PNGV model, and the Sage-Husa adaptive filtering algorithm is fused to unscented Kalman filter (UKF) algorithm, the noise is predicted and corrected in real time, so as to improve the estimation accuracy of SOC. The simulation results show that AUKF is closer to the theoretical reference value than the UKF estimate. AUKF solves the problem of error due to fixed noise in the UKF, which can improve the battery current under complicated conditions such as starting, cruising and braking of the electric vehicle Estimated accuracy of SOC in drastic changes.