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.

锂离子电池广泛用于电动汽车。内部电池温度的实时预测对于准确的电池管理系统控制至关重要。本文旨在优化集总参数热模型，使其能够精确呈现热流分布并实现在线电池温度估算的广泛适用性。提出了用于硬壳锂离子电池的三种集总参数热模型，包括两态集总热模型（2STM），五态集总热模型（5STM）和改进的五态集总热模型（5STM +）。5STM +考虑了5STM表面状态之间的热传递。通过基于实验数据求解线性方程和最小二乘拟合的非线性曲线来确定模型参数。通过评估七个可能的应用情况下的计算准确性，可以确定模型的适用性。发现2STM和5STM在统一的边界条件下表现出色，但是导致局部液体冷却方案存在显着差异。5STM +在所有提议的情况下都提供了很高的计算精度，包括自然对流，强制空气对流，局部液体冷却以及局部加热。同时，计算时间消耗仅为毫秒级。在线电池内部温度估算非常实用。5STM +在所有提议的情况下都提供了很高的计算精度，包括自然对流，强制空气对流，局部液体冷却以及局部加热。同时，计算时间消耗仅为毫秒级。在线电池内部温度估算非常实用。5STM +在所有提议的情况下都提供了很高的计算精度，包括自然对流，强制空气对流，局部液体冷却以及局部加热。同时，计算时间消耗仅为毫秒级。在线电池内部温度估算非常实用。