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Abstract 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.
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State-of-Health Estimation for Sustainable Electric Vehicle Batteries Using Temporal-Enhanced Self-Attention Graph Neural Networks
Abstract Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graphs. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and gated recurrent unit (GRU) models.
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- Award ID(s):
- 2324950
- PAR ID:
- 10543946
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Energy Resources Technology
- Volume:
- 146
- Issue:
- 6
- ISSN:
- 0195-0738
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Electric vehicles (EVs) are considered an environmentally friendly option 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 aid in the prevention of 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 is capable of capturing 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 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 effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4065146
