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Spectroscopy Measurement

Optimizing the effect of battery relaxation on electrochemical impedance spectroscopy measurement for real-time SOC estimation using transfer learning


Conventional Electrochemical Impedance Spectroscopy (EIS) measurements require extended battery rest periods, restricting real-time use in battery management systems (BMS) and limiting generalization across chemistries. This work introduces transfer learning to significantly shorten rest time and improve model adaptability, enabling more practical and scalable EIS integration for real-world state of charge (SOC) estimations.
EIS experiments were performed on 52 Ah Lithium Iron Phosphate (LFP) and 3.6 Ah Nickel Cobalt Manganese (NCM) cells at varying SOCs, with and without rest periods. The transfer learning-based Deep Neural Network (DNN-TL) model achieved high SOC estimation accuracy for LFP cells with mean squared error (MSE) of 0.0063 and mean absolute error (MAE) of 0.0664, improving MSE by 77.58% and MAE by 50.92% compared to standard models. Additionally, only 30% of the original dataset size was needed for retraining. Applying the DNN-TL model trained on LFP data to NCM cells using unrested EIS data resulted in up to 82.08% reduction in MSE and 53.15% in MAE, requiring only 20% of the original data size for retraining.
Extensive EIS experiments were conducted on both 52 Ah LFP and 3.6 Ah NCM cells in a laboratory environment to obtain measurement data with and without rest time. For LFP cells, the proposed deep neural network with transfer learning achieves reliable SOC estimation using unrested EIS data, with an MSE of 0.0063 and MAE of 0.0664. This represents a significant improvement of up to 77.58% in MSE and 50.92% in MAE compared to conventional DNN models trained without transfer learning. 
Moreover, the burden of EIS data collection is substantially reduced—only 30% of the original source dataset is needed to retrain the DNN model while maintaining high accuracy. To further enhance the applicability of data-driven SOC estimation across different battery chemistries, the DNN-TL model trained on LFP cells is transferred to NCM cells. Using unrested EIS data, the transfer learning approach yields notable improvements in SOC estimation accuracy, achieving reductions of up to 82.08% in MSE and 53.15% in MAE relative to a standalone DNN trained solely on NCM data. 
Additionally, the retraining process requires only 20% of the original NCM dataset, significantly reducing data collection effort. These findings demonstrate the effectiveness of transfer learning in enabling real-time, cross-chemistry SOC estimation with reduced rest time and minimal data. Future investigations should address critical operating conditions encountered in EV applications, including variations in battery design, temperature-dependent EIS behavior, and the impact of battery aging and cycle life on EIS data distribution to support deployment in more dynamic and diverse scenarios.

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