A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter

Abstract The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One most important issues is lithium-ion (SOC) Compare with extended Kalman filter algorithm, this paper proposed a novel adaptive square root together Thevenin equivalent circuit model which can solve problem filtering divergence caused by computer rounding errors. It uses Sage-Husa to update noise variable, and performs decomposition covariance matrix ensure its non-negative definiteness. Moreover, multi-scale dual algorithm used for joint estimation SOC capacity; forgetting factor recursive least-square method parameter identification. To verify feasibility under complicated operating conditions, different types dynamic working conditions are performed ternary battery. has robust accurate results eliminate errors improve adaptability compared conventional algorithm.