Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches - Talal Alharbi; Muhammad Umair; Abdulelah Alharbi (IEEE Access)
Timely prediction of the State of Health (SoH) of lithium-ion batteries is important for battery management and longevity. Traditional centralized deep learning models have shown promising results, but they raise concerns related to data privacy, as data needed to be collected and trained on a single node. This study addresses this challenge by utilizing both centralized (i.e., deep learning) and decentralized (i.e., federated learning) approaches for SoH prediction. The NASA battery dataset, containing charging and discharging cycles, is used for model training and evaluation. Three deep learning architectures 1D Convolutional Neural Networks (CNN), CNN plus Long Short-Term Memory (LSTM), and CNN plus Gated Recurrent Units (GRU) are used in the centralized approach. The 1D CNN model outperforms, demonstrating strong predictive capabilities, thus for decentralized learning (i.e., federated learning), the 1D CNN model is utilized with federated averaging technique across five clients, allowing for local training without sharing raw data. Obtained results shows that the highest testing RMSE (0.666) and MAPE (0.980) are observed during decentralized learning, while the centralized approach shows varying performance across different batteries. The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.