The accurate online estimation of cell state of charge in electric vehicles is challenging due to extensive computational requirement, measurement noise and convergence issues. This paper proposes an adaptive extended Kalman filter based state of charge determination method, which employs improved simulated annealing method to access the uncertain states and adaptive switch mechanism to adjust the estimation algorithm. It combines the advantages of the local linear approximation and noise reduction capability from extended Kalman filter with the global optimization from improved simulated annealing. The method is verified by the samples collected from battery test system. Results illustrate that the proposed method estimates the cell remained capacity with great performance despite a range of common errors from initialization, current disturbance and measurement noise.

由于广泛的计算需求，测量噪声和收敛性问题，电动汽车中电池电荷状态的准确在线估计具有挑战性。提出了一种基于自适应扩展卡尔曼滤波器的荷电状态确定方法，该方法采用改进的模拟退火算法访问不确定状态，并采用自适应开关机制调整估计算法。它结合了扩展卡尔曼滤波器的局部线性逼近和降噪能力以及改进的模拟退火算法的全局优化优势。该方法通过从电池测试系统收集的样品进行验证。结果表明，尽管初始化产生了一系列常见错误，但该方法仍能以较高的性能估算出电池的剩余容量，