Temperature Prediction of Lithium-Ion Batteries Based on Physical Information and Deep Neural Network
Year: 2024
Type: article
Abstract: Accurately predicting the temperature of lithium-ion batteries is a key technology for battery management systems. A deep neural network is constructed for temperature prediction of lithium-ion batteries based on their dynamic as well as time-dependent characteristics. The model can extract the potential high-dimension features of the data and appropriately reduce their dimensionality to reduce the model complexity while capturing the long-term dependence of temperature through the layer of long short-term memory cells. In addition, the heat generation rate is calculated in real-time through the open circuit voltage, terminal voltage and current of the lithium-ion battery, thus providing additional physical information input to the deep neural network. The results show that the method has better temperature prediction performance compared to other methods.
Authors: Laien CHEN, Xiaoyong ZENG, Zihao ZENG, Caichen CHENG, Yaoke SUN
Institution: Changsha University of Science and Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6884763395
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