Electrochemical battery model and its parameter estimator for use in a battery management system of plug-in hybrid electric vehicles

Abstract

This paper reports the development of a battery model and its parameter estimator that are readily applicable to automotive battery management systems (BMSs). Due to the parameter estimator, the battery model can maintain reliability over the wider and longer use of the battery. To this end, the electrochemical model is used, which can reflect the aging-induced physicochemical changes in the battery to the aging-relevant parameters within the model. To update the effective kinetic and transport parameters using a computationally light BMS, the parameter estimator is built based on a covariance matrix adaptation evolution strategy (CMA-ES) that can function without the need for complex Jacobian matrix calculations. The existing CMA-ES implementation is modified primarily by region-based memory management such that it satisfies the memory constraints of the BMS. Among the several aging-relevant parameters, only the liquid-phase diffusivity of Li-ion is chosen to be estimated. This also facilitates integrating the parameter estimator into the BMS because a smaller number of parameter estimates yields the fewer number of iterations, thus, the greater computational efficiency of the parameter estimator. Consequently, the BMS-integrated parameter estimator enables the voltage to be predicted and the capacity retention to be estimated within 1 % error throughout the battery life-time.