关键词: 锂电池；残差重采样；无迹卡尔曼滤波；粒子滤波；健康状态

Abstract: In order to accurately estimate the state of health (SOH) of lithium-ion batteries, this paper proposes an SOH evaluation method based on an improved particle filter algorithm. In order to solve the problem that the particle weight tends to zero in the traditional particle filter algorithm and leads to the loss of particle diversity, the residual resampling algorithm is introduced to replace the traditional resampling method by separating the integer and decimal parts of the particle weight, so as to reduce the particle degradation phenomenon and maintain the diversity of the particle set. At the same time, the unscented Kalman filter (UKF) algorithm is combined to generate Sigma points based on state mean and covariance to capture the uncertainty of the system state more accurately and avoid the truncation error of local linearization approximation. The experimental data published by NASA laboratory are used for verification, and the results show that compared with the traditional particle filter algorithm, the proposed method reduces the average error to less than 2%, and significantly improves the accuracy and robustness of SOH estimation.

Key words: lithium battery; residual resampling; unscented Kalman filter; particle filter; state of health