State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network

被引:0
|
作者
Zhao, Yixin [1 ]
Behdad, Sara [1 ]
机构
[1] Univ Florida, Environm Engn Sci, Gainesville, FL 32611 USA
来源
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME | 2024年 / 146卷 / 10期
基金
美国国家科学基金会;
关键词
electric vehicle batteries; state of health estimation; transformer networks; alternative energy sources; energy storage systems; LITHIUM-ION BATTERIES; USEFUL LIFE PREDICTION; CHARGE; VALIDATION; MODELS;
D O I
10.1115/1.4065762
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Electric vehicles (EVs) are considered an environmentally friendly option compared to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and prevent dangerous occurrences. Data-driven models with advantages in time-series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The transformer model can capture long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard transformer and an encoder-only transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from the NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can improve the model's accuracy and computational efficiency. The proposed standard transformer shows good performance in the SOH prediction.
引用
收藏
页数:12
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