Battery lifetime prediction using surface temperature features from early cycle data

Abstract

Lithium-ion batteries (LIBs) are highly sensitive to cycling conditions and show a nonlinear degradation pattern, typically noticeable in later stages. This affects the accuracy of most battery health prognostic models, especially those relying on long-term data collected under varying operational conditions. To tackle these challenges, we propose using statistical features extracted from the battery surface temperature during the first 10 cycles and developing a data-driven machine learning (ML) model for early-cycle lifetime prediction. Models are trained on each of the selected open-source datasets comprising 223 LIBs and tested on their respective datasets with non-stratified data splits using a balanced ratio. These datasets include lithium iron phosphate (LFP), nickel cobalt aluminum oxide (NCA), and nickel manganese cobalt oxide (NMC) cells, tested under different environmental temperatures and cycling protocols. In one comprehensive dataset, our model achieved competitive performance compared to state-of-the-art studies that rely on features extracted from much longer cycling data—up to ten times the duration. This work provides valuable insights into the strong correlation between early-cycle surface temperature and battery lifetime across various battery chemistries, cycling rates, and environmental temperatures.