Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Zhao, Jingyuan; Feng, Xuning; Wang, Junbin; Lian, Yubo; Ouyang, Minggao; Burke, Andrew F.

doi:10.1016/j.apenergy.2023.121949

Cited by 22 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the research applied to power battery fault diagnosis is relatively rare, which has a wide range of application prospects. From the above literature, it is clear that SSAEs can effectively extract data features and increase data differentiation in the case of complex sample data so that the dataset performs better in the ensuing fault diagnosis task [19]. Furthermore, the combination of the capsule network and attention mechanism is capable of effectively mitigating the impact of noise generated by an SSAE on network performance.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

View full text Add to dashboard Cite

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

View full text Add to dashboard Cite

show abstract

“…Conventional monitoring techniques may be inadequate in rapidly and properly identifying abnormalities, underscoring the need for sophisticated technology, including machine learning, to tackle these ever-changing difficulties. [6][7][8][9][10] The main aim of this project is to construct and assess a machine learning-driven anomaly detection system specifically designed for electric transportation networks. The system endeavors to scrutinize charging station data, encompassing power consumption, charging logs, and station utilization patterns, with the objective of detecting abnormalities such as energy spikes, protracted charging intervals, and aberrant energy declines.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Anomaly Detection in Electric Transportation Networks

Usanova,

Rani,

Mishra

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.

show abstract

Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Wang,

Chen

2024

Applied Energy

View full text Add to dashboard Cite

Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Cited by 22 publications

References 50 publications

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Machine Learning for Anomaly Detection in Electric Transportation Networks

Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Contact Info

Product

Resources

About