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BATT2GRAPH: A Hybrid CNN-LSTM and Temporal Graph-Based Approach for Lithium-Ion Battery SOH Prediction and Anomaly Detection

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Hajer Akid
Mohamed Wadhah Mabrouk
Slimane Arbaoui
Ahmed Samet
Boudour Ammar
Published in
Information Integration and Web Intelligence
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces BATT2GRAPH, a groundbreaking approach that combines a temporal graph database with a hybrid CNN-LSTM model to predict the State of Health (SOH) of lithium-ion batteries (LIBs) and detect anomalies. The text delves into the global transition towards sustainable energy systems and the pivotal role of LIBs in this shift, highlighting their advantages and the challenges they face regarding longevity and safety. The chapter presents a novel hybrid data-driven approach for LIB SOH prediction and anomaly detection, detailing the design and implementation of a temporal graph-based data model using Neo4j. It also proposes an enriched hybrid CNN-LSTM model that combines raw time-series features with aggregated statistical indicators to improve SOH prediction accuracy. The experimental results demonstrate that BATT2GRAPH consistently outperforms baseline models, achieving SOH prediction with root mean squared error (RMSE) values below 1%. The chapter concludes with a discussion on the potential of the temporal graph model for monitoring battery health and outlines future research directions.
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previous chapter Addressing Label Scarcity: Hybrid Anomaly Detection in Mental Healthcare Billing
next chapter CoRA: Continual Learning for Multimodal Sensing with a Case Study in Mental Health
Title
BATT2GRAPH: A Hybrid CNN-LSTM and Temporal Graph-Based Approach for Lithium-Ion Battery SOH Prediction and Anomaly Detection
Authors
Hajer Akid
Mohamed Wadhah Mabrouk
Slimane Arbaoui
Ahmed Samet
Boudour Ammar
Copyright Year
2026
Book
Information Integration and Web Intelligence

Print ISBN: 978-3-032-11975-9

Electronic ISBN: 978-3-032-11976-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-11976-6_9
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