Papers A fuzzy robust two-stage unscented Kalman filter method for uncertainty and state of charge estimation of lithium-ion batteries

For the practical state of charge (SoC) estimation of lithium-ion batteries, the unknown battery model uncertainties should be considered. Conventionally, the robust SoC estimation method uses a bounded matrix to model battery uncertainties. However, since the upper bound of the uncertainty is unknown, the robust estimation would be conservative and inaccurate. To accurately estimate the SoC of lithium-ion batteries, this paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method to model the battery uncertainty with unknown statistical characteristics. The proposed estimator is able to estimate the model uncertainties and does not require the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, and the destructive effect of the uncertainties on the estimation accuracy is eliminated correctly. To make the estimation more accurate and practical during experiments, the covariance matrix of the measurement noise is recursively adjusted using a fuzzy system. In addition, as the battery aging increases the model uncertainty, the proposed observer is able to adjust the SoC estimation at each battery life period using the estimated uncertainties. In the first stage, the state variables of the battery model are estimated. The covariance matrix of the measurement noise is recursively adjusted using a fuzzy system, which makes the estimation more accurate and practical during experiments. In the second stage, the unknown model uncertainties are estimated. Based on the estimated uncertainties, state variables, including SoC, are adjusted to compensate for the adverse effects of the model inaccuracy on the state estimation. The experimental results show that in addition to the ability for the battery model uncertainty estimation, the proposed estimator is more accurate, as much as 1 % and more robust than other Kalman-based methods for aged batteries with more uncertain models.