Battery Lifetime Prediction and Degradation Analysis with Machine Learning Method

Abstract

Due to environmental pollution and climate crisis caused by fossil fuels, eco-friendly alternative energy sources are gaining attention. Particularly, electric vehicles (EVs) are being recognized as the next-generation means of transportation. The most crucial technologies in electric vehicles are lithium-ion batteries and Battery Management Systems (BMS). However, the extended lifespan of lithium-ion batteries leads to prolonged assessment periods, making it challenging to measure and improve performance over time. Additionally, the operation of batteries under various conditions makes it difficult to measure state of charge (SOC) and state of health (SOH) in real-time, introducing many variables. With recent advancements in predictive technology through machine learning, accurate predictions can be made without complex physical knowledge, leveraging high-quality data.In this study, machine learning techniques were applied using charge/discharge data from lithium-ion LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cell batteries. The values measured during the initial charge/discharge cycles were converted into variables that could assess battery degradation modes. Machine learning algorithms were then applied, followed by model tuning to reduce overfitting and enhance accuracy. This research contributes to real-time battery management system data using machine learning technology to predict the remaining useful life (RUL) of lithium-ion batteries with high accuracy, using only initial cycles. This approach enhances the efficiency of BMS data in real-world scenarios and contributes to the overall time efficiency of battery research. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT(NRF-2021K1A4A8A01079455).