Article
Data-Driven Analysis of Energy Efficiency in Battery Electric City Buses
The electrification of transportation, especially city buses, is becoming increasingly important for achieving sustainable mobility and reducing environmental impact. However, designing and operating battery electric buses (BEBs) efficiently requires a deep understanding of real-world driving conditions, including variations in speed, passenger load, traffic congestion, and route characteristics. One of the major challenges in this domain is accurately predicting energy demand, as uncertainties often lead to overly conservative system designs, resulting in higher costs, oversized batteries, and reduced operational efficiency. This study addresses these challenges by introducing a set of novel explanatory variables that effectively capture speed patterns and driving behavior. These variables provide a more detailed representation of real-world conditions and are integrated into advanced machine learning models to enhance prediction accuracy. A total of five different algorithms were developed and evaluated, considering factors such as prediction accuracy, robustness, computational efficiency, and practical applicability. The experimental results demonstrate that the proposed models achieve prediction accuracy above 94%, indicating their strong capability in modeling complex energy consumption patterns. Additionally, the approach reduces reliance on complex physical simulations, making it more scalable and suitable for real-time applications. This enables fleet operators to make better decisions regarding route planning, battery sizing, and charging infrastructure placement. Overall, the proposed methodology contributes to cost reduction, improved operational efficiency, and supports the wider adoption of clean and sustainable public transportation systems. Furthermore, the proposed framework can be easily adapted to different cities and operational scenarios, making it highly flexible for future deployments. It also opens opportunities for integrating real-time data analytics and smart transportation systems, enabling continuous monitoring and optimization of energy usage. This adaptability ensures long-term benefits in evolving urban mobility environments.
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