Estimation of Remaining Useful Life of Lithium-Ion Batteries and Output Uncertainty Evaluation

In recent years, batteries, particularly lithium-ion batteries, have become essential to a wide range of applications, from everyday technologies like portable electronics and electric vehicles to industrial applications. Estimating a battery's Remaining Useful Life (RUL), i.e., the number of charge and discharge cycles it can perform before needing replacement, is a critical aspect of predictive maintenance. Accurately determining a battery's RUL, especially as it approaches its End of Life (EoL), enhances system reliability, improves maintenance practices, and helps reduce costs. Furthermore, in industrial environments, providing clear explanations of Artificial Intelligence (AI) model outputs is essential to building trust in AI, guaranteeing safety, and facilitating smoother decision-making, as these outputs directly influence operational processes. In this paper, we propose a novel set of features based on temperature and capacity data, features commonly found in public datasets, to predict RUL. Additionally, we introduce the Monte Carlo dropout technique during inference to enrich the model's output. This approach provides not only the predicted RUL values but also the standard deviation and distribution of the predictions, making the decision process more transparent and reliable.