Optimization of the lumped parameter thermal model for hard-cased li-ion batteries

Abstract

Abstract Li-ion batteries are widely employed for electric vehicles. Real-time prediction of the internal battery temperature is crucial for accurate battery management system control. This paper intends to optimize the lumped parameter thermal model so that it is able to precisely present heat flow distribution and achieve broad applicability for online battery temperature estimation. Three lumped parameter thermal models for hard-cased Li-ion batteries are proposed, including the two-state lumped thermal model (2STM), five-state lumped thermal model (5STM), and improved five-state lumped thermal model (5STM+). The 5STM+ considers the heat transfers between the surface states of the 5STM. The model parameters are identified through solving linear equations and nonlinear curves fitting in the least square sense based on experimental data. Model applicability is confirmed by the evaluation of calculation accuracy under seven possible application situations. It is found that 2STM and 5STM behave excellently with uniform boundary conditions, but lead to significant discrepancy for local liquid cooling scenarios. 5STM+ provides high calculation accuracy under all proposed situations, covering natural convection, forced air convection, regional liquid cooling as well as localized heating. Meanwhile, the calculation time consumption is only a millisecond level. It is practical for online battery internal temperature estimation.