Core Temperature Estimation for Self-Heating Automotive Lithium-Ion Batteries in Cold Climates

Abstract

The onboard battery self-heaters are employed to improve the performance and lifetime of the automotive lithium-ion batteries under cold climates. The battery performance is determined by the core temperature which is significantly higher than the surface temperature during the fast self-heating, while only the surface temperature can be directly measured. By estimating the core temperature to monitor the self-heating condition, the heating time and the energy consumption can be improved. However, the high-frequency heating current and the time-variant battery impedance cannot be measured in real time by a low-sampling-rate battery management system, so that the regular core temperature estimation methods are not applicable during the self-heating. To solve the issues, an online core temperature estimation algorithm based on the lumped thermal-electrical model is developed for the onboard ac self-heater. By implementing an extended state observer to compensate for the effect of the parameter uncertainties, the core temperature can be accurately detected even with the unknown internal resistance and root mean square (RMS) heating current. The experimental validation of 18 650 lithium-ion batteries shows that the core temperature estimation error is within only 1.2 °C. As a result, the self-heating time and energy consumption can be reduced by 50%.