Research on variable time-scale SOC and SOH asynchronous collaborative estimation strategy for electric vehicle power lithium iron phosphate batteries

Abstract

Battery state parameters such as state-of-health (SOH) have slow time-varying characteristics, while state-of-charge (SOC) parameters have fast time-varying features. Using the same time scale algorithm framework to estimate the above state parameters simultaneously will waste BMS system calculation and cause severe parameter fluctuations due to frequent parameter updates, affecting the estimation accuracy. To address the above issues, this work proposes a variable time-scale SOC and SOH asynchronous collaborative estimation strategy for automotive power lithium iron phosphate batteries. Among them, the real-time changing battery state SOC is estimated through micro time scale filtering algorithms, the slowly changing battery parameter SOH is estimated through macro time scale filtering algorithms, and the time scale parameters that can minimize the cost function when maximizing noise are obtained through the robust optimization strategy of minimum maximization differential evolution. The results indicate that the asynchronous collaborative estimation strategy of variable time scale SOC and SOH proposed in this paper can effectively balance the accuracy of SOC estimation and SOH estimation.