Artificial intelligence-based predictive reference model for lithium iron phosphate battery cell aging analysis

This study introduces an Artificial intelligence (AI) approach to model the discharge voltage characteristics of a new Lithium-Iron Phosphate (LFP) battery cell under different operating conditions and to use it as a reference for healthy assessment. Experimental voltage-State Of Charge (SOC) data were obtained from a new cell at three temperatures (0°C,25°C, and 45°C) and for several discharge currents. In order to predict the appropriate discharge voltage behavior under any operating conditions, a Gaussian Process Regression (GPR) model was trained using temperature, discharge current, and SOC as input variables. The trained model provides a continuous voltage reference under any realistic combination of temperature and current. Based on this reference, a diagnostic system was developed to compare the measured discharge voltage of cycled cells with the reference voltage of a new cell under the same conditions. The deviation between the predicted and measured voltages enables the estimation of State of Health (SOH) and allows assessing whether a manufactured cell exhibits early degradation. This approach provides a fast and efficient solution for cell quality assessment and early detection of abnormal degradation. The results demonstrate that the proposed AI based reference model enables reliable SOH evaluation, offering strong potential for industrial diagnostic applications and manufacturing quality control.