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The rapid adoption of electric vehicles (EVs) underscores the growing need for reliable battery health monitoring systems to ensure safety, optimize performance, and extend operational lifespan. In this paper, we introduce BATT2GRAPH, a novel approach that combines a temporal graph-based representation with a CNN-LSTM predictive model for accurate State-of-Health (SOH) estimation and anomaly detection in lithium-ion batteries (LIBs). On one hand, BATT2GRAPH constructs a temporal property graph using Neo4j to store enriched charge-discharge cycles with both raw time-series data and aggregated statistical indicators, enabling interpretable SOH monitoring and anomaly detection through expressive Cypher queries. On the other hand, a hybrid CNN-LSTM model is trained on this data to capture fine-grained variations and long-term degradation trends. Extensive experiments on the Stanford-MIT battery aging dataset demonstrate that our approach consistently outperforms existing baselines across multiple evaluation metrics.

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

  • 会议论文

  • 发表日期：

    2026年

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  • Hajer Akid,
  • Mohamed Wadhah Mabrouk,
  • Slimane Arbaoui,
  • Ahmed Samet,
  • Boudour Ammar

摘要

The rapid adoption of electric vehicles (EVs) underscores the growing need for reliable battery health monitoring systems to ensure safety, optimize performance, and extend operational lifespan. In this paper, we introduce BATT2GRAPH, a novel approach that combines a temporal graph-based representation with a CNN-LSTM predictive model for accurate State-of-Health (SOH) estimation and anomaly detection in lithium-ion batteries (LIBs). On one hand, BATT2GRAPH constructs a temporal property graph using Neo4j to store enriched charge-discharge cycles with both raw time-series data and aggregated statistical indicators, enabling interpretable SOH monitoring and anomaly detection through expressive Cypher queries. On the other hand, a hybrid CNN-LSTM model is trained on this data to capture fine-grained variations and long-term degradation trends. Extensive experiments on the Stanford-MIT battery aging dataset demonstrate that our approach consistently outperforms existing baselines across multiple evaluation metrics.