Driver Behavior–Based Optimization of Electric Bus Energy Consumption via Bio-Inspired WUTP Algorithm and Real-Time Data Analytics

DOI

10.2991/978-94-6239-668-5_32How to use a DOI?

Keywords

Regenerative braking systems; Big data in transportation; Transport energy efficiency; Sustainable urban mobility; Artificial intelligence in transit systems

Abstract

The transition toward sustainable urban transportation necessitates not only advances in electric bus (E-Bus) technology but also the optimization of operational variables that directly affect system efficiency. Among these factors, driver behavior plays a pivotal role in determining energy consumption and overall range performance. This study presents an advanced artificial intelligence–based optimization framework that combines real-time big data analytics with a bio-inspired algorithm modeled on Water Uptake and Transport in Plants (WUTP). The proposed system was applied to hybrid trolleybus-type E-Buses operating in Malatya, Turkey, utilizing a dataset of approximately 50 million measurements collected under varying climatic, topographical, and operational conditions. After comprehensive preprocessing and correlation analysis, fourteen dominant parameters—including regenerative braking rate, auxiliary loads (HVAC and static converters), acceleration, and road gradient—were identified as the principal contributors to energy usage. Using a representative dataset of 60,000 samples, the WUTP algorithm generated optimized driving patterns and adaptive weighting coefficients, allowing the estimation of ideal operational thresholds. The results demonstrate that sustaining regenerative braking efficiency above 77%, maintaining moderate accelerator input around 44%, and ensuring steady vehicle speed significantly enhance driving range while lowering energy demand. Comparative driver analyses revealed performance variations exceeding 30%, emphasizing the necessity for intelligent training and monitoring systems. The developed framework, characterized by its adaptability to different routes and environmental conditions, offers a scalable tool for fleet energy management, eco-driving evaluation, and battery capacity planning in sustainable public transportation networks.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yunus Emre Ekici
AU  - Nisanur Yıldıran
AU  - Ozan Akdağ
AU  - Teoman Karadağ
PY  - 2026
DA  - 2026/05/14
TI  - Driver Behavior–Based Optimization of Electric Bus Energy Consumption via Bio-Inspired WUTP Algorithm and Real-Time Data Analytics
BT  - Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
PB  - Atlantis Press
SP  - 299
EP  - 307
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-668-5_32
DO  - 10.2991/978-94-6239-668-5_32
ID  - Ekici2026
ER  -