Jian Zhao
Verified @msn.com
Scopus Publications
- Multi-depot electric vehicle routing problem with drones considering time-dependent traffic
Hao Fan, Kai Liu, Jiangbo Wang, Jian Zhao
Advanced Engineering Informatics, 2026 - Robust electric bus charging in photovoltaic-energy storage systems with dual uncertainties
Hong Gao, Kai Liu, Jian Zhao, Liyuan Zhao
Transportation Research Part D Transport and Environment, 2025 - Berth allocation and tugboat scheduling problem for tidal ports with compound channels: The case of Tianjin port
Hao Fan, Tian-Hui Zhang, Jian Zhao, Li-Jun Yue
Ocean Engineering, 2025 - Energy Consumption Prediction for Electric Buses Based on Traction Modeling and LightGBM
Jian Zhao, Jin He, Jiangbo Wang, Kai Liu
World Electric Vehicle Journal, 2025
In the pursuit of sustainable urban transportation, electric buses (EBs) have emerged as a promising solution to reduce emissions. The increasing adoption of EBs highlights the critical need for accurate energy consumption prediction. This study presents a comprehensive methodology integrating traction modeling with a Light Gradient Boosting Machine (LightGBM)-based trip-level energy consumption prediction framework to address challenges in power system efficiency and passenger load estimation. The proposed approach combines transmission system efficiency evaluation with dynamic passenger load estimation, incorporating temporal, weather, and driving pattern features. The LightGBM model, hyperparameter tuned through Bayesian Optimization (BO), achieved a mean absolute percentage error (MAPE) of 3.92% and root mean square error (RMSE) of 1.398 kWh, outperforming traditional methods. SHAP analysis revealed crucial feature impacts on trip-level energy consumption predictions, providing valuable insights for operational optimization. The model’s computational efficiency makes it suitable for real-time IoT applications while establishing precise parameters for future optimization strategies, contributing to more sustainable urban transit systems.
