The quick and accurate estimation of the state of health (SOH) of Li-ion battery is a technical difficulty in battery management system research. For the low accuracy of Li-ion battery SOH estimation under complex stress conditions, an estimation method of SOH for Li-ion battery using the adaptive dual extended Kalman filter-based fuzzy inference system (ADEKF-FIS) is proposed. First, Li-ion battery SOH is online estimated by dual extended Kalman filter. Then the Sage–Husa adaptive algorithm and the fuzzy controller are used to correct the state noise covariance and the observed noise covariance, respectively. The algorithm is flat on the state variance and the noise variance. The recursive estimation of the square root ensures the symmetry and nonnegative nature of the state and noise variance. In the end, this paper performing the dynamic stress test condition experiment for confirmation. Experimental results show that, compared with the EKF algorithm, ADEKF-FIS algorithm can obtain state of charge estimation with higher accuracy, which further improves the prediction accuracy of SOH and makes this algorithm have higher accuracy and better convergence.

快速准确地估计锂离子电池的健康状态（SOH）是电池管理系统研究中的技术难题。针对复杂应力条件下锂离子电池的SOH估算精度低的问题，提出了基于自适应双扩展卡尔曼滤波模糊推理系统（ADEKF-FIS）的锂离子电池SOH估算方法。首先，通过双扩展卡尔曼滤波器在线估算锂离子电池SOH。然后使用Sage-Husa自适应算法和模糊控制器分别校正状态噪声协方差和观测噪声协方差。该算法在状态方差和噪声方差上是平坦的。平方根的递归估计可确保状态和噪声方差的对称性和非负性。到底，本文进行了动态应力测试条件实验以供确认。实验结果表明，与EKF算法相比，ADEKF-FIS算法可以获得更高的荷电状态估计，进一步提高了SOH的预测精度，使该算法具有更高的准确性和更好的收敛性。