State-of-Health Estimation for Lithium-Ion and Sodium-Ion Battery Cells Using Data-Driven Models and Cross-Chemistry Transfer Learning

Le résumé fourni par la source

The rapid expansion of green energy systems has accelerated the adoption of battery energy storage systems (BESS) and increased the demand for advanced battery management system (BMS). While lithium-ion batteries (LIBs) dominate current applications, concerns over resource limitations and cost have motivated growing interest in sodium-ion batteries (SIBs) as a more abundant and economical alternative. State of health (SOH) estimation remains essential for ensuring safe and reliable operation, yet most existing research focuses on LIBs due to the limited availability of high-quality SIB ageing datasets. This study conducted comprehensive ageing tests on LIBs and SIBs, obtained a unified dataset, and analyzed how their health declined over time, along with detailed patterns in their charging behavior, based on incremental capacity analysis (ICA) curve, to understand degradation. Using these features, seven deep-learning models, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM) networks, gated recurrent units (GRU), and three hybrid CNN–recurrent architectures were developed and evaluated under a Leave-One-Cell-Out (LOCO) scheme. For LIBs, recurrent and CNN–recurrent hybrid models achieved the best accuracy, reaching a minimum mean absolute error (MAE) of 0.8811%. In contrast, simpler architectures such as DNN and CNN were more effective for SIBs, with the best model achieving a minimum MAE of 0.2591%. Transfer learning (TL) from LIB to SIB demonstrated promising feasibility: several model–cell combinations showed unchanged or improved accuracy, although larger degradation-pattern mismatches in some SIB cases led to MAE increases of up to 1.4106%. Overall, the findings highlight shared electrochemical ageing characteristics between LIBs and SIBs and demonstrate the potential of leveraging extensive LIB datasets to support SOH estimation for emerging SIB technologies.

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