A method for the lifetime sensorless estimation of surface and core temperature in lithium-ion batteries via online updating of electrical parameters

Online temperature estimates are essential to the thermal monitoring and control of battery cells for battery management systems (BMSs). Due to hardware limitations, there has been a surge in interest in sensorless approaches for both surface and core temperatures. On this account, several methods have been proposed in the literature for the coestimation of state of charge (SOC) and temperature via RC-based electrical and thermal models and Extended Kalman Filters (EKFs). However, the stability and reliability of these schemes over the complete cell lifetime, when the effects of battery aging become apparent, have not been addressed thoroughly. In this article, a dual state-parameter estimation is carried out on an enhanced equivalent circuit model to coestimate the SOC and SOH on a commercial nickel-rich, silicon–graphite cell throughout its entire lifetime. A thermal model has been characterized based on the previous electrical model for the estimation of surface and core temperature of the cell. The continuous updating and correction of electrical parameters prove to be critical for temperature estimations to remain accurate in the long run, yielding a root mean square error (RMSE) in surface temperature below 1.2 °C for as long as 800 cycles.