Аннотация
Proper forecasting of remaining useful life (RUL) of lithium-ion batteries is critical for safety, operational stability, and cost-effectiveness in modern electric vehicles. Because battery degradation arises from complex electrochemical and thermal processes, data-driven approaches have become a practical alternative to physics-based modeling. This study develops and critically compares supervised machine learning models, including Random Forest, Gradient Boosting, Linear Regression, Ridge Regression, K-Nearest Neighbors, Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN), for cycle-level RUL estimation using a publicly available lithium-ion battery dataset. The dataset comprises engineered voltage-time features, capacity, and charge–discharge indicators, which are preprocessed using Min–Max normalization to support stable training and fair model comparison. Among the evaluated methods, the Random Forest regressor achieved the best overall performance, reaching an R2 of 0.9998 with lower MAE and RMSE than both traditional regressors and deep learning architectures. The results indicate that ensemble tree-based models are especially effective at capturing nonlinear degradation behavior when trained on engineered tabular features rather than raw time-series signals. While the DNN and CNN achieved high R2 values, their error metrics were comparatively larger, suggesting that deep architectures may benefit from higher-resolution time-series inputs or alternative sequence modeling formulations. Finally, the top-performing model was integrated into an end-to-end prediction system using FastAPI, SQLite, and a responsive web dashboard to demonstrate real-world applicability. The deployment allows estimating RUL in real time and at lowlatency and provides stakeholders with an accessible tool for battery health monitoring. Overall, the study shows that ensemble learning techniques can deliver strong performance for battery condition monitoring and provides a deployable architecture to practical RUL prediction in electric-vehicle battery management systems.