Multi-step Prediction of Battery Temperature and Thermal Fault Diagnosis Based on Informer

2025-01-7019

01/31/2025

Features
Event
SAE 2024 Vehicle Powertrain Diversification Technology Forum
Authors Abstract
Content
Lithium-ion batteries are prone to thermal failures under extreme conditions, leading to thermal runaway and safety risks such as fire or explosion. Therefore, effective temperature prediction and diagnosis are crucial. This paper proposes a thermal fault diagnosis method based on the Informer time series model. By extracting temperature-related features and conducting correlation analysis, a 9-dimensional input parameter matrix is constructed. Experimental results show that the model can maintain an absolute temperature prediction error within 0.5°C when predicting 10 seconds in advance, with higher accuracy than the LSTM model. Additionally, a three-level warning mechanism based on the forgetting coefficient further enhances diagnostic accuracy. Validation using test data and real vehicle data demonstrates that this method can efficiently diagnose and locate thermal faults in batteries, with low computational costs, making it suitable for online applications.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-7019
Pages
10
Citation
Sun, Y., Zhu, X., Zhang, Z., Peng, Z. et al., "Multi-step Prediction of Battery Temperature and Thermal Fault Diagnosis Based on Informer," SAE Technical Paper 2025-01-7019, 2025, https://doi.org/10.4271/2025-01-7019.
Additional Details
Publisher
Published
Jan 31
Product Code
2025-01-7019
Content Type
Technical Paper
Language
English