İçeriğe geç
akaturk Akademik ölçüm

Makale detayı · 2025 · article

The Impact of Driver Behavior on Electric Bus Energy Consumption: Optimizing Driver Performance with Bio-Inspired WUTP Algorithm with Real-Time Big Data

YÖKSİS OpenAlex Açık erişim · diamond SJR Q3 TR Index
Yıl2025
Atıf3OpenAlex
Yüzdelik%69,1
FWCI0,731,00 = dünya ortalaması
Scopus (SJR)Q3

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıInternational Journal of Automotive Science And Technology
  • Katalog eşleşmesi (ISSN)International Journal of Automotive Science and Technology
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex İngilizce

The transition toward sustainable urban mobility requires not only technological innovations in electric buses (E-Buses) but also optimization of operational factors such as driver behavior, which significantly influences energy consumption and driving range. This study develops a novel artificial intelligence framework, integrating real-time big data with a bio-inspired Water Uptake and Transport in Plants (WUTP) algorithm, to optimize E-Bus driver performance under real-world conditions. Data were collected from trolleybus-type hybrid electric buses operating in Malatya, Turkey, encompassing nearly 50 million observations across diverse seasonal, topographical, and operational contexts. Through preprocessing and correlation-based feature selection, 14 key parameters—including regenerative braking, auxiliary loads (HVAC and static converters), acceleration, and road slope—were identified as critical determinants of energy consumption. The WUTP algorithm, implemented with 60,000 representative data rows, generated optimized driving profiles and weighting coefficients, enabling precise estimation of optimal operational thresholds. Results reveal that maintaining regenerative braking above 77%, moderating accelerator pedal use at approximately 44%, and stabilizing average vehicle speed significantly extend range and reduce energy demand. Comparative evaluation of six drivers demonstrated efficiency disparities exceeding 30%, underscoring the importance of training and monitoring systems. The proposed model is distinguished by its dynamic treatment of auxiliary loads, scalability across routes and climates, and applicability for fleet planning, battery sizing, and eco-driving assessment. Overall, this research contributes a robust, adaptable, and scalable framework that enhances operational efficiency, reduces environmental impact, and supports the broader deployment of sustainable E-Bus systems in global transit networks.

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

3atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yerel katalogda bu makaleye atıf yapan 2 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. 2026 A Decision Tree-Based Dynamic Maintenance Interval Model Incorporating Driver Behavior and Traffic Congestion: An Application to an Electric Bus FleetAtıf 0 · OpenAlex
  2. 2026 Traffic Aware 1 Hz Energy Modeling and Regenerative Braking Analysis of E-Bus Operations Using Real-World DataAtıf 0 · OpenAlex

Yazarlar

4
  1. YUNUS EMRE EKİCİ 1
  2. OZAN AKDAĞ 2
  3. NİSANUR YILDIRAN İNÖNÜ ÜNİVERSİTESİ 3
  4. TEOMAN KARADAĞ İNÖNÜ ÜNİVERSİTESİ 4