ProADD: Proactive battery anomaly dual detection leveraging denoising convolutional autoencoder and incremental voltage analysis

The use of lithium-ion batteries is increasing fast in many fields such as electric vehicles (EVs) and energy storage system (ESS). However, the number of accidents caused by thermal runaway of lithium-ion batteries is also increasing. Hence, it is critical to diagnose lithium-ion batteries proactiv...

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Veröffentlicht in:Applied energy Jg. 373; S. 123757
Hauptverfasser: Jeon, Jihun, Cheon, Hojin, Jung, Byungil, Kim, Hongseok
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2024
Schlagworte:
Anomaly detection
batteries
Denoising autoencoder
electric potential difference
electrochemistry
Energy storage system
guidelines
Incremental voltage analysis
Lithium-ion battery
support vector machines
Lithium-ion battery
Denoising autoencoder
Energy storage system
Anomaly detection
Incremental voltage analysis
ISSN:0306-2619
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Zusammenfassung:The use of lithium-ion batteries is increasing fast in many fields such as electric vehicles (EVs) and energy storage system (ESS). However, the number of accidents caused by thermal runaway of lithium-ion batteries is also increasing. Hence, it is critical to diagnose lithium-ion batteries proactively for safe operation. This paper considers both electrochemical model and deep learning model to capture the intrinsic characteristics of battery and diagnose its state from complementary perspectives. First, the denoising autoencoder (DAE) is leveraged to detect outliers in latent space clustering. Second, the traditional incremental capacity analysis (ICA) is revisited and incremental voltage analysis (IVA) is proposed to make it suitable for real-time ESS operation. Then, a method is proposed that jointly considers the DAE error and the IVA peak to proactively detect anomaly battery modules of ESS. Specifically, one-class support vector machine (OCSVM) is leveraged as well as the transformed Z-score. Our results confirm that the proposed framework named ProADD clearly identifies and quantifies anomaly modules, which provides a guideline for safe ESS operation in real fields. •Propose proactive anomaly detection to overcome challenges in unlabeled, actual ESS.•Propose incremental voltage analysis (IVA) to achieve data resolution robustness.•ProADD guarantees flexibility in handling intermittent battery operations.•ProADD combines two complementary methods for anomaly detection & scoring.
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ISSN:0306-2619
DOI:10.1016/j.apenergy.2024.123757