Internal Temperature Estimation for Lithium-ion Batteries Through Distributed Equivalent Circuit Network Model

Abstract

Lithium-ion batteries commonly experience significant internal thermal gradients during operation which have a direct impact on the safety, performance, cost and lifetime of a cell. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical lithium-ion cell (LG M50TNMC811). The model is parameterized and comprehensively validated against experimental data for 21700 cylindrical cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant current-switching scenarios. The developed model is used to estimate core temperature based on the surface temperature measurement; its predictions are shown to have good accuracy at relatively low computational cost when using a standard computer. We show that the widely adopted two/three-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharge conditions, when thermal gradients become higher. Compared to the standard three-node model, the distributed Equivalent Circuit Network model (dECN) contains the effects of features such as detailed internal cell structure (electrode, current collector, metal can and tab) and distributed internal heat generation. The results are of immediate interest to both cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.