Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Accurate battery state of charge (SOC) estimation can contribute to safe and reliable utilization of the battery. However, commonly used battery model-based SOC estimation methods suffer from the lack of a universal battery model for cells in a battery pack since the model parameters of each cell are inevitably different from each other and variable with battery aging, leading to difficulties in promoting the model-based methods for real applications. To solve this problem, a differential voltage (DV) analysis based universal battery model and two associated SOC estimation algorithms using extended Kalman filter (EKF) and particle filter (PF), respectively, are proposed in this paper. By means of a natural cubic interpolation approach, a battery SOC-DV model is firstly derived from the SOC based DV curves of various cells at different aging levels. A novel battery model-based scheme is then proposed to incorporate the SOC-DV model for the estimation. The robustness of the proposed approaches against different cell aging levels is evaluated, and the promising SOC estimates with the maximum absolute error of 1.75% and the root mean square error of less than 1.10% can be achieved.