BATTERY HEALTH INDEX MONITORING USING GATED RECURRENT UNITS AND VARIATIONAL AUTOENCODERS: A MACHINE LEARNING APPROACH TO PREDICTIVE ANALYTICS

Abstract

Lithium iron phosphate (LiFePO4) batteries have many applications in portable gadgets, energy storage devices, and electric cars because to their long cycle life, reliability, and safety. Nevertheless, usage pattern, temperature, depth of discharge, charging rate and maintenance practices influence battery health in years to come. This project creates a machine learning driven system to give an estimate about the lifetime of LiFePO4 batteries by predicting the SOH and SOC. The system uses Gated Recurrent Units (GRU) in order to capture temporal dependencies in historical performance data, and uses Variational Autoencoders (VAEs) for dimensionality reduction and anomaly detection. The system gathers and analyzes important information of batteries such as usage history, environmental status, charging behaviors. Various data preprocessing approaches such as cleaning, normalization and anomaly handling are used to capture the correct model. GRUs process sequential data to predict SOH and SOC, and their use in the prediction process increases prediction efficiency by data simplification and outlier identification. The predicted SOH and SOC are presented with intuitive graphical outputs for the user to monitor the health and accumulative state of the battery over time. Also, it is possible to follow the system in real-time, and the system raises an alarm upon reaching the critical levels of SOH and SOC, which allows for proactive maintenance decisions. By optimizing maintenance timetables and life span of the lithium batteries this system increases energy conservation and also increases the efficiency of LiFePO 4 in numerous applications.