Fast charging method based on estimation of ion concentrations using a reduced order of Electrochemical Thermal Model for lithium ion polymer battery

Comparably long charging time for battery of electric and hybrid vehicles is one of barriers for massive commercialization of the vehicles. Typical charging methods are by a constant current (CC) with constant voltage (CV), pulsed or tapered current. Theoretically, the charging time can be reduced by increased amplitude of the charging current, which, however, accelerate degradation of cells and reduces the lifespan. The relationship between the charging current and the degradation has not been well understood. Studies on ion transport and chemical reactions using a computational model developed in our laboratory reveal that a high charging current causes excessive ions at the surface of electrode particles because of slow diffusion process of ions in the solid electrodes. The excessive lithium ions react with electrons and form a thin layer, called Lithium plating that is irreversible. The Lithium plating not only reduces ion conductivities, but also contributes growth of dendrites and potentially internal short circuit. In this paper, a new charging algorithm is proposed that is based on an electrochemical and thermal model, which order is drastically reduced in order to facilitate a real time operation. The model, called Reduced Order of Electrochemical Thermal Model (ROM), is completely validated with a pouch type of Lithium polymer battery and used to dynamically estimate ion concentration at the surface of particles. Based on the estimated ion concentration, a new control algorithm is derived that allows for determination of amplitude and duration of the charging current. The ROM performs at least ten fold faster in calculations than the original full order model. The simulation and experimental results show that the charging time can be reduced to 60-70% of that of the classical CC/CV charging by preventing excessive ions and slowing down degradation of cell capacity losses.