State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle type, rely on controlled Lab conditions, and often lack explainability mechanisms. To overcome all these limitations, this paper proposes a more practically applicable and explainable SoC estimation framework that jointly addresses vehicle diversity (bus and passenger EVs), battery chemistry variability (Nickel Cobalt Manganese and Lithium Iron Phosphate), collective battery dynamics, and real-world on-road operational uncertainties within a unified learning architecture. This study introduces MIL-LGBM, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint. An experimental study conducted on a real-world dataset demonstrated that the proposed framework achieved a 0.799 MAE for passenger vehicles across a wide range of on-road driving conditions.
Range estimation for electric vehicles (EVs) is critical for intelligent transportation systems since it directly affects charging planning, route optimization, driver confidence, energy management, battery utilization, and driver decision-making processes. However, current studies still suffer from issues such as limited accuracy, insufficient interpretability, high computational complexity, dependence on simulation environments, or insufficient generalization capability under dynamic driving conditions. To address these limitations, this paper proposes an M5Boost framework that successfully integrates an additive residual learning methodology with the model tree structure. Unlike conventional boosting approaches, M5Boost combines iterative residual-driven learning, multivariate leaf regression models, tailored tree pruning, and specific smoothing mechanisms to improve prediction accuracy, robustness, and generalization capability for EV range estimation. A benchmark dataset was further systematically extended with newly collected real-world battery-related driving records. Experimental validation showed that the developed model significantly outperformed state-of-the-art models reported in the literature on the same dataset.
I. Kubilay, K. Balbal, K. Birant et al.· Batteries· 0 citations
