Online Health Estimation of Batteries with Moderate to High Degradation Utilizing LSTM-Ensembled Learning Framework from Consecutive CC Charging Segments

Vollständiger Abstract

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Accurate online state of health (SoH) estimation, especially for highly degraded second-life batteries (SLBs), is critical for the safe and efficient operation of any Battery Management System (BMS). However, existing data-driven estimation methods typically rely on complete charge/discharge cycles or assume a fixed starting state of charge (SoC) and fixed voltage windows. These assumptions are highly restrictive and rarely align with the random, partial charging behaviors of real-world electric vehicle (EV) operations and Battery Energy Storage System (BESS) applications. To overcome this limitation, this paper presents the following framework: an online SoH estimation approach utilizing long short-term-memory (LSTM)-ensembled learning using consecutive constant current (CC) charging segments. Instead of relying on a rigid voltage window, the presented method utilizes nested, expanding slices of highly flexible CC charging intervals to capture sequential degradation dynamics. Furthermore, this study maps suggestive ranges of optimal voltage intervals that dynamically adapt to different battery health conditions, ensuring high estimation accuracy despite the type of degradation. The framework is validated using four commercially available lithium-ion batteries (LIBs), with a moderate to low SoH down to ~40%. To ensure practical viability, the hybrid deep neural network (DNN) and LSTM architecture has been highly optimized, requiring only 474 trainable parameters. The framework has been evaluated utilizing multiple performance matrices, showing minimal discrepancy with excellent correlation to the true SoH throughout the lifespan of the testing cell.