M5Boost: A Machine Learning Approach for Driving Range Estimation in Electric Vehicles Considering Battery-Related Factors
Batteries, cilt.12, sa.7, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12 Sayı: 7
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/batteries12070256
- Dergi Adı: Batteries
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, Directory of Open Access Journals, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: battery, boosting, electric vehicles, machine learning, range estimation, regression, transportation systems
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
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.