Improved single particle model based state of charge and capacity monitoring of lithium-ion batteries

State of charge and state of health monitoring of lithium-ion batteries is a hot topic in the area of battery management. Although much work has been done for state estimation based on equivalent circuit model, more research is needed to monitor battery state using electrochemical model which can reflect chemical reactions inside the battery. In this paper, an online state of charge and capacity estimation strategy is proposed based on improved single particle model using extended Kalman filter. Firstly, an improved single particle model which incorporates Li-ion concentration distribution in electrolyte phase is established. Then two extended Kalman filters with different time scales based on the model are used to estimate state of charge and capacity. Finally, the ability of the method to against erroneous initial values is evaluated, and the experimental results show the feasibility of the proposed approach.