Joint Estimation for Battery Capacity and the State of Charge Based on Variable Time Scale

As the core energy source of electric vehicles, power batteries directly restrict the development of electric vehicles. Accurate estimation of SOC is not only the fundamental function of the electric vehicle battery management system but also helps to improve energy utilization of batteries, safeguard the application of batteries in EVs, and extend the cycling life. However, the time-varying nonlinearity, environmental sensitivity, and irreversible decay during the use of the battery make the estimation of hidden states such as SOC a challenge to the industry. This study conducted the following research on the SOC and capacity estimation of lithium-ion batteries: (1) To achieve the co-estimation of the battery’s state and parameters, an adaptive cubature Kalman filter SOC estimation method based on random weighting (ARWCKF) is proposed, at the same time, Extended Kalman Filter (EKF) is used to identify the parameter on-line. The results verify that this approach has a better performance with the error of SOC being under 3%. (2) Aiming at the limitations of the single-time-scale joint estimation algorithm, taking accumulated discharge as the conversion standard between micro and macro time scales. The filtering performance of the algorithm is effectively evaluated based on the prediction accuracy of the terminal voltage, SOC, capacity, and the convergence rate of SOC and capacity, verifying that compared to the single-time-scale approach, this approach has better robustness and accuracy.