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Poster: Big Data and Discrete Optimization for Electric Urban Bus Operations

  • This paper proposes a framework for optimizing an electric urban bus network, based on data of an existing diesel bus fleet. Therefore, the required input data is described as well as the required steps to analyze and preprocess the data from the busses. With this aggregated data a mathematical model was formulated to optimize the cost of a future electric bus network. This is accomplished by choosing optimal batterie sizes and charging station locations as well as charging powers. • With extensive real-world data logging, we could validate assumptions about energy demand, waiting times and different traffic situations during the day • To use the data for the model implementation of a robust data pipeline including Interpreting, preprocessing and aggregating the data, was necessary • Based on the data of the diesel busses we simulated the electric buses energy demand • The Integer Linear Programming model finds cost optimal solutions for charging infrastructure and batterie sizes • The algorithm is very adjustable to specific needs ofThis paper proposes a framework for optimizing an electric urban bus network, based on data of an existing diesel bus fleet. Therefore, the required input data is described as well as the required steps to analyze and preprocess the data from the busses. With this aggregated data a mathematical model was formulated to optimize the cost of a future electric bus network. This is accomplished by choosing optimal batterie sizes and charging station locations as well as charging powers. • With extensive real-world data logging, we could validate assumptions about energy demand, waiting times and different traffic situations during the day • To use the data for the model implementation of a robust data pipeline including Interpreting, preprocessing and aggregating the data, was necessary • Based on the data of the diesel busses we simulated the electric buses energy demand • The Integer Linear Programming model finds cost optimal solutions for charging infrastructure and batterie sizes • The algorithm is very adjustable to specific needs of public transport operators and can generate optimal solutions in a short amount of timeshow moreshow less

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Metadaten
Author:Samuel WürtzORCiD, Klaus Bogenberger, Ulrich GöhnerORCiD
DOI:https://doi.org/10.13140/RG.2.2.13335.70563
Parent Title (English):Transportation Research Board Annual Meeting 2022
Document Type:conference proceeding (presentation)
Conference:101st Transportation Research Board Annual Meeting 2022, 9.-13. January 2022, Washington D.C.
Language:English
Date of Publication (online):2022/01/01
Year of first Publication:2022
Number of pages:1 Folie
Institutes:IFA – Institut für Innovative Fahrzeugantriebe
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Open Access:open_access
Research focus:FSP1: Energie
FSP2: Mobilität
Publication Lists:Göhner, Ulrich
Würtz, Samuel
Publication reviewed:nicht begutachtet
Release Date:2023/03/15
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