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Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

  • As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energyAs the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.show moreshow less

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Metadaten
Author:Samuel WürtzORCiD, Klaus Bogenberger, Ulrich GöhnerORCiD, Andreas RuppGND
DOI:https://doi.org/10.3390/wevj15010027
Identifier:2032-6653 OPAC HS OPAC extern
Parent Title (English):World Electric Vehicle Journal
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2024/01/12
Year of first Publication:2024
Tag:auxiliary power models; electric buses; energy consumption forecasting; propulsion power analysis; public transportation electrification
Volume:15
Issue:1
Number of pages:27 Seiten
Institutes:Fakultät Informatik
Fakultät Maschinenbau
IFA – Institut für Innovative Fahrzeugantriebe
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP1: Energie
FSP2: Mobilität
Publication Lists:Rupp, Andreas
Göhner, Ulrich
Würtz, Samuel
Publication reviewed:begutachtet
Release Date:2024/01/19
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