Sequential estimation of state of charge and equivalent circuit parameters for lithium-ion batteries

We propose a method to estimate the state of charge (SoC) and the equivalent circuit parameters for lithium-ion batteries. Model-based approaches for SoC estimation, such as Kalman filter, achieve better accuracy than Coulomb counting or open circuit voltage method, albeit requiring accurate model parameters of the battery. We analyze bias errors in the Kalman filter-based SoC estimation induced by errors of the battery model parameters, and develop a simultaneous recursive least squares filter to produce unbiased estimation of the battery parameters.