Model-Based Approach to Long Term Prediction of Battery Surface Temperature

Abstract

Temperature has a substantial impact on the safety, efficiency, and reliability of a lithium-ion (Li-ion) battery. The objective of a battery thermal management system (BTMS) is to maintain the battery in preferred temperature range. State-of-the-art BTMS is designed to react to the measured temperature at the battery surface, whereas research suggests that the performance of BTMS will likely improve when used in conjunction with predicted temperature. This article focuses on surface temperature prediction of Li-ion batteries based on an equivalent circuit model-based approach. Particularly, this article presents theoretically sound approaches to model heat generation in the core of the battery and its propagation to its surface. Another novel aspect of the present article is that the heat generation and propagation model identification is tested in various practical scenarios, where the measurement noise is significant. Experimental results using data collected from high precision battery cycler and temperature sensor show an open-loop mean absolute error of 1.678 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C during a high-current discharge that lasted approximately 800 s. Using the theoretical models presented in this article, the performance battery temperature prediction error at low SNR conditions can be understood.