Thermal Modeling and Prediction of The Lithium-ion Battery Based on Driving Behavior

Tingting Wang, Xin Liu, Dongchen Qin and Yuechen Duan
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Tingting Wang: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Xin Liu: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Dongchen Qin: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Yuechen Duan: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China

Energies, 2022, vol. 15, issue 23, 1-22

Abstract: Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data, which can predict the thermal behavior of the battery in real-world driving cycles, are proposed and established by MATLAB/Simulink tool. Driving behaviors analysis of different drivers are simulated by PI control as input, and battery temperature is used as output response. The results show that aggressive driving behavior leads to an increase in battery temperature of nearly 1.2 K per second, and the average prediction error of TECM model and ANN model is 0.13 K and 0.11 K, respectively. This indicates that both models can accurately estimate the real-time battery temperature. However, the computational speed of the ANN thermal model is only 0.2 s, which is more efficient for battery thermal management.

Keywords: lithium-ion battery; electro-thermal model; driver behavior; temperature prediction; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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