Estimation of Distributed Temperature of Large-format Fast-charging Lithium-ion Batteries Based on a Model-data Fusion Method

doi:10.3901/JME.2023.22.020

Abstract

Abstract: Problems such as long charging time, short mileage, and poor thermal safety are becoming the major factors hindering the large-scale penetration of electric vehicles. High-specific-energy battery technologies and high-safety and damage-free fast charging are becoming popular, reflecting in the larger-sized battery cell and rapidly increased charging power. However, the temperature unevenness within the large-format lithium-ion battery is evident and high-power charging is prone to cause a rapidly elevated temperature and even thermal runaways. Therefore, it is of great importance to propose an accurate and efficient temperature estimation method for fast-charging batteries. A fusion method integrating a long short-term memory(LSTM) network and a heat generation model is proposed for estimating the distributed temperature of pouch-type lithium-ion batteries. The generalizability of the method is verified in a wide temperature ranging from 5 to 40 °C and a variety of fast charging scenarios. This method accurately estimates the temperatures of remaining key measurement locations on the battery plane based on the temperature information of only one measurement location. It can not only obtain the approximate two-dimensional temperature distribution of large-format batteries, including the highest temperature and maximum temperature difference, but greatly reduce the cost of temperature sensors when battery grouping. The fusion model takes the output of the heat generation model as the input of the LSTM model, and the effects of the measurement location and heat generation model on the fusion model’s accuracy are discussed. The results show that the selection of temperature measurement locations has a great impact on the prediction accuracy of the model. Compared to the positive tab temperature, taking the temperature measurement at the connection of the positive tab and main battery body as the input can reduce the root mean square error(RMSE) of the remaining measurement locations by 50%, and the maximum RMSE is only 0.239 °C. Compared to the model without heat generation as its input, the heat generation model optimally simulating distributed heat generation of the battery can reduce the RMSE of the model by about 11%.

Key words: pouch-type lithium-ion battery, fast charging, heat generation model, data-driven, estimation of distributed temperature

CLC Number:

U469

PANG Xiaoqing, LI Jiacheng, LIU Wenxue, DENG Zhongwei, HU Xiaosong. Estimation of Distributed Temperature of Large-format Fast-charging Lithium-ion Batteries Based on a Model-data Fusion Method[J]. Journal of Mechanical Engineering, 2023, 59(22): 20-32.

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