Accurate SoC estimation of lithium-ion batteries based on parameter-dependent state-space model

This paper is concerned with the State-of-Charge (SoC) estimation of lithium-ion batteries based on an equivalent circuit model and the extended Kalman filtering technique. The physical parameters in the equivalent circuit are dependent on both the temperature and the SoC of the battery, though the previous method assume the parameters to be constant. We propose a new method for improving the estimation accuracy based on a parameter-dependent state-space model. To be more specific,we derive a parameter-dependent state-space model by viewing these physical parameters as time-varying parameters,and then apply the extended Kalman filter to estimate the SoC. The effectiveness of the proposed method is verified by experimental results.