Improved Coupled Electrothermal Model of Lithium-ion Battery for Accurate Core Temperature Estimation at High Current

• Published in: IEEE transactions on consumer electronics p. 1
• Main Authors: Sinha, Shiv Shankar, Nambisan, Praveen, Khanra, Munmun
• Format: Journal Article
• Language: English
• Published: IEEE 16.08.2024
• Subjects: Accuracy; Batteries; electrothermal model (ETM); Entropy; entropy coefficients; improved coupled electrothermal model (ICETM); Integrated circuit modeling; Lithium-ion battery (LIB); Mathematical models; State of charge; Thermal resistance; three-state thermal model (TSTM)
• ISSN: 0098-3063
• DOI: 10.1109/TCE.2024.3445769

Lithium-ion batteries (LIBs) are a widely used energy storage technology owing to their excellent energy density, minimal self-discharge property, and high cycle life. Despite these promising features, their performance is affected by both low and high temperatures. When the internal temperature exceeds a certain threshold, the battery may experience thermal runaway, leading to fire and explosion. Moreover, this process is accelerated at high charge/discharge currents. Therefore, in high current applications, accurate monitoring of the internal temperature of the battery becomes critically important to ensure the safety. Hence, an improved coupled electrothermal model (ICETM) has been proposed by combining a novel three-state thermal model with an existing electrical equivalent circuit model through temperature dependent electrical parameters and heat generation. The primary aim is to improve the accuracy of internal temperature estimation of the battery at high currents while accounting for time efficiency in thermal model parameterization. The ICETM is parameterized through experimental and simulation studies using a LiFePO4/graphite battery. The effectiveness of the proposed model and parameterization method is validated experimentally using two case studies. The results show 14% improvement in accuracy and 140-160 hours time reduction over its existing counterparts in estimating core temperature and model parameterization, respectively.