Comprehensive Modelling and Prediction of State of Health for 18650 Lithium Titanate Oxide, Lithium Manganese Oxide, and Lithium Iron Phosphate Batteries Using Machine Learning Algorithms

In this study, the state of health (SoH) estimation of lithium-ion batteries (LIBs), which have become an indispensable part of modern life due to their widespread use in portable electronics and electric vehicles (EVs), has been conducted. Experimental data have been collected from three different battery chemistries—lithium titanate oxide (LTO), lithium manganese oxide (LMO), and lithium iron phosphate (LFP)—all in 18650 cylindrical format. These cells have been aged through charge-discharge cycling under constant current-constant voltage (CC-CV) conditions using a battery analyzer. Specifically, 2000, 400, and 1000 cycles have been completed for LTO, LMO, and LFP cells, respectively. The acquired datasets have been extracted, cleaned, and pre-processed, then subjected to differential voltage analysis (DVA), a data-driven technique widely utilized for SoH assessment. The DVA has proven effective in identifying detailed degradation patterns by analyzing subtle voltage changes during cycling. In this context, machine learning (ML) algorithms—namely Linear Regression (LR), Support Vector Machine (SVM), and Gaussian Process Regression (GPR)—have been integrated with DVA to improve the predictive accuracy of SoH estimation. This study considers SoH estimation as a regression task, and the combined use of DVA and ML represents a central novelty of the work. Based on regression analyses conducted in MATLAB, LR has provided the most accurate results for LTO and LMO, while the Matern 5/2 kernel-based GPR model has performed best for LFP. The corresponding RMSE values—1.5005 (LTO), 1.246 (LMO), and 1.667 (LFP)—reflect the predictive precision of each approach. These results demonstrate that SoH estimation has been successfully carried out with low error margins. The study confirms that combining DVA with appropriate regression models yields SoH predictions exceeding 98% prediction performance. This highlights the feasibility of data-driven, chemistry-specific diagnostics in battery health monitoring for EVs and ESSs. All results have been comparatively evaluated.