Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter

This paper proposes a state of charge (SOC) estimation method for lithium-ion batteries. Firstly, a fractional second-order RC circuit model of the battery is established. Then, a particle swarm optimization algorithm with a linear differential decline strategy is adopted to identify the model parameters, and the accuracy of the parameterized model is verified. Finally, an adaptive fractional-order square root unscented Kalman filter (AFSR-UKF) is developed, which is able to update the noise information in real time and to overcome divergence caused by inappropriate noise covariance matrices. The effectiveness of the SOC estimation method based on the AFSR-UKF is verified under a variety of operational conditions in the perspective of the root-mean-squared error, which is shown to be below 1.0%, including at extreme temperatures, revealing good accuracy and robustness. (c) 2022 Published by Elsevier Ltd.

Automated summary

This paper proposes an SOC estimation method for lithium-ion batteries using a particle swarm optimization algorithm and an unscented Kalman filter, showing good accuracy and robustness under various operational conditions.