Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

Lithium-ion batteries play a crucial role in powering various applications, including Electric Vehicles (EVs), underscoring the importance of accurately estimating their State Of Health (SOH) throughout their operational lifespan. This paper introduces two novel models: a Transformer (TOPS-SoH) and a Long Short-Term Memory based (LSTM-OSoH) for One-shot Prediction of SOH. The LSTM-OSoHexcels in accuracy, achieving a Masked Mean Absolute Error (MMAE) of less than 0.01 for precise SOH estimation, while the TOPS-SoHdemonstrates simplicity and efficiency, with accuracy comparable to state-of-the-art models. The TOPS-SoHmodel also offers additional interpretability by providing insights into the attention scores between inputs and outputs, highlighting the cycles used for estimation. These models were trained using the MIT battery dataset, with auto-encoders employed to reduce the dimensionality of the input data. Additionally, the models’ effectiveness was validated against a Bidirectional LSTM ( BiLSTM ) baseline, demonstrating superior performance in terms of lower MMAE, MMSE, and MAPE values, making them highly suitable for integration into Battery Management Systems (BMS). These findings contribute to advancing SOH estimation up to the End Of Life (EOL), which is crucial for ensuring the reliability and longevity of lithium-ion batteries in diverse applications. • The paper introduces two deep learning models, TOPS-SoH and LSTM-OSoH, specifically designed for one-shot prediction of the State of Health (SOH) of Lithium-Ion Batteries (LIBs) until reaching the first End Of Life (EOL). • Auto-encoders constructed to reduce input data dimensionality. • The research emphasizes model explainability, with the TOPS-SoH model’s multihead attention mechanism highlighting the input cycles used for estimation. • The experiments are conducted on the battery dataset published by the Massachusetts Institute of Technology (MIT).