IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 597 10.51583/IJLTEMAS.2026.151000078 Machine Learning Predicting Electric Bus Energy Consumption Using Explainable Machine Learning Madeti Rahul School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh 522237, India India K. Revanth Kumar Reddy C. School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh 522237, India India Rajat Rajat School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh 522237, India India Cholika Dodda School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh 522237, India India Syed Khasim School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh 522237, India India 15 10 959 976 24 10 2026 29 10 2026 07 11 2026 battery electric bus energy consumption prediction gradient boosting SHAP explainable machine learning range assurance smart charging synthetic data Abstract

Battery electric buses (BEBs) must finish every trip on a fixed store of energy, so a poor trip-level forecast costs either a stranded bus or a needless charging stop. This paper presents a reproducible pipeline that predicts trip energy intensity (kWh/km) from seven pre-trip variables, explains each prediction with SHAP (SHapley Additive exPlanations), and passes the forecast to a pre-dispatch range-assurance check and a time-of-use depot-charging scheduler, all served through a 13-endpoint REST interface. Real fleet telemetry was not available, so the study uses two physics-based synthetic benchmarks: a 10,459-trip RouteZero-style set (RZ-Syn) and an 8,500-trip Indian-conditions set (IN-Syn). Nineteen physically motivated features feed four regressors. On six held-out buses (1,864 trips), histogram gradient boosting reaches MAE 0.0374 kWh/km, RMSE 0.0541 kWh/km, R² 0.9916 and MAPE 3.45%, against a generator noise ceiling of R² 0.9949; over 20 re-drawn bus splits MAPE is 3.44 ± 0.08%, and the Indian benchmark gives R² 0.9829. Stress tests temper these figures: withholding the hillside regime raises MAPE to 6.43%, transfer between benchmarks raises it above 20% with systematic under-prediction, and a per-variable input-range check does not reliably detect either. The nominal 95% interval covers only 86.1% of test trips; split-conformal recalibration restores 94.5%. In a low-battery dispatch test the worst-case range check missed none of 735 infeasible trips, whereas a static average-consumption rule missed 10.3%. Smart charging cut simulated electricity cost by 32–71%. Validation on measured fleet data is the necessary next step

 References B. Sturmberg, J. N. Hendriks, et al., “An integrated model of electric bus energy consumption and optimised depot charging,” npj Sustain. Mobil. Transp., 2024, doi: 10.1038/s44333-024-00008-2. H. Basma, C. Mansour, M. Haddad, M. Nemer, and P. Stabat, “Comprehensive energy modeling methodology for battery electric buses,” Energy, vol. 207, Art. no. 118241, 2020. H. Abdelaty, A. Al-Obaidi, M. Mohamed, and H. E. Z. Farag, “Machine learning prediction models for battery-electric bus energy consumption in transit,” Transp. Res. D, Transp. Environ., vol. 96, Art. no. 102868, 2021. H. Abdelaty and M. Mohamed, “A prediction model for battery electric bus energy consumption in transit,” Energies, vol. 14, no. 10, Art. no. 2824, 2021. S. Nan, R. Tu, T. Li, J. Sun, and H. Chen, “From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus,” Energy, vol. 261, Art. no. 125188, 2022. S. Nan, F. Liao, T. Li, H. Chen, and J. Sun, “Identifying the electricity-saving driving behaviors of electric bus based on trip-level electricity consumption: A machine learning approach,” Environ. Sci. Pollut. Res., vol. 30, no. 34, pp. 82743–82759, 2023. W. Wang, M. Mohamed, B. Zhang, Q. Zhang, and H. Abdelaty, “Electric bus energy prediction and factors interactions using explainable machine learning models,” Eng. Appl. Artif. Intell., vol. 163, Art. no. 112877, 2026. J. Vepsäläinen, A. Ritari, A. Lajunen, K. Kivekäs, and K. Tammi, “Energy uncertainty analysis of electric buses,” Energies, vol. 11, no. 12, Art. no. 3267, 2018. M. Gallet, T. Massier, and T. Hamacher, “Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks,” Appl. Energy, vol. 230, pp. 344–356, 2018. M. Almajed, M. Hassan, T. D. Turjo, M. A. Islam, M. E. Haque, and M. M. H. Rahman, “Explainable machine learning for electric bus energy prediction under tropical urban conditions: A physics-informed parametric framework,” Energy Eng., vol. 123, no. 10, 2026, doi: 10.32604/ee.2026.084024. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. Adv. Neural Inf. Process. Syst., vol. 30, 2017, pp. 4765–4774. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2016, pp. 785–794. G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Proc. Adv. Neural Inf. Process. Syst., vol. 30, 2017, pp. 3149–3157. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Statist., vol. 29, no. 5, pp. 1189–1232, 2001. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. D. R. Roberts et al., “Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure,” Ecography, vol. 40, no. 8, pp. 913–929, 2017. S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, “Leakage in data mining: Formulation, detection, and avoidance,” ACM Trans. Knowl. Discovery Data, vol. 6, no. 4, Art. no. 15, 2012. A. Lajunen, “Energy consumption and cost-benefit analysis of hybrid and electric city buses,” Transp. Res. C, Emerg. Technol., vol. 38, pp. 1–15, 2014. K. R. Kambly and T. H. Bradley, “Estimating the HVAC energy consumption of plug-in electric vehicles,” J. Power Sources, vol. 259, pp. 117–124, 2014. Road Vehicles—Vehicle to Grid Communication Interface—Part 1: General Information and Use-Case Definition, ISO Standard 15118-1, 2019.