To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0–1 s and 1–10 s. The extended Kalman filter (EKF) algorithm was improved to present a novel improved Kalman filter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.

为了解决在各种工作条件下由于实时估算困难和精度低而导致的锂离子电池健康状态（SOH）估算问题，分析了由内部电阻增加引起的SOH变化。基于二阶RC等效电路模型，考虑了充电状态（SOC）对内电阻的短期影响，该影响是在放电条件下设置的。此外，内部电阻的变化以0–1 s和1–10 s的两个间隔进行了分析。对扩展卡尔曼滤波器（EKF）算法进行了改进，以提出一种新颖的改进卡尔曼滤波器（IKF）算法，以准确预测不同工作条件下的长期内阻。建立了基于内阻增加的计算公式，并估算了SOH。与基于内部电阻增加的遗忘因子最小二乘法相比，将计算结果的误差在HPPC条件下控制在4.0％以内，在BBDST条件下控制在3.0％以内，在DST条件下控制在6.0％以内。所提出的算法具有良好的收敛性，有助于改善SOH估计，并鼓励锂离子电池的应用。