State of Charge and Health Estimation For Lithium-Ion Batteries Using Recursive Least Squares

Abstract

The state of charge (SOC) and state of health (SOH) of lithium-ion batteries (LIB) are two major indices of the battery management system (BMS) for system monitoring, health prognosis, and optimal usage. This paper presents a new SOC and SOH estimation method based on two recursive least squares (RLS) algorithms. First, a second-order equivalent circuit model is used to describe battery dynamics. Second, an open-circuit-voltage (OCV) and internal resistance estimation method is proposed based on the RLS algorithm. Instead of using SOC as a state in a state space model, the OCV is estimated from a linear regression directly. The battery total capacity is then estimated by a combination of the estimated OCV and another RLS algorithm. The accurate battery SOC and SOH can be obtained without a priori knowledge of battery parameters. Simulation results highlight the accuracy and robustness of the proposed method.