Estimation of battery parameters and state of charge using continuous time domain identification method

Battery equivalent circuit models (ECMs) are widely used in battery management systems (BMSs), such as in electric vehicles (EVs). The battery terminal voltage-current (VI) dynamics and the ECM parameters depend on the operating conditions, such as the state of charge (SOC) and temperature. Online parameter estimation can improve not only the modelling accuracy, but also the performance of modelbased SOC estimation, which plays a key role in BMS. This paper presents a continuous-time (CT) domain algorithm for online co-estimation of the battery ECM parameters and SOC, using the linear integral filter (LIF) method. Compared with the conventional discrete time domain least square algorithm, the proposed CT LIF technique has superior performance at capturing the battery slow dynamics, which can further improve the SOC estimation accuracy. Experimental data are collected using a Li ion battery (LIB), and the results are analyzed to verify the efficacy of the proposed algorithm.