TY - JOUR
T1 - Charging strategy selection for electric bus systems
T2 - A multi-criteria decision-making approach
AU - Sadrani, Mohammad
AU - Najafi, Amirhossein
AU - Mirqasemi, Razieh
AU - Antoniou, Constantinos
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Selecting the best type of charging strategy, among a variety of options such as overnight (slow) charging and opportunity (fast) charging methods, is a crucial step in electrifying bus networks. This step has gained increased importance with the growing need for sustainable transportation solutions and the widespread adoption of electric buses (EBs) in public transport systems. This study develops a multi-criteria decision-making (MCDM) approach for selecting the best EB charging strategy, considering a comprehensive range of criteria, including economic, environmental, social, operational, and quality-of-service criteria. A thorough literature review and a survey of EB experts are conducted to identify key decision-making factors in this area. A Fuzzy Best-Worst Method (FBWM) is designed to determine the weight of criteria, and a Fuzzy Ranking of Alternatives through Functional mapping of criterion subintervals into a Single Interval (FRAFSI) method is designed to rank available charging strategies for EB systems in Munich, Germany. Results show that the most crucial consideration for decision-makers is the economic aspect, followed by operational and environmental factors. The infrastructure cost is the most crucial factor in the economic category, followed by battery cost and operational cost. The driving range is the most crucial factor in the operational category, followed by charging duration and energy monitoring. The experts’ assessments indicate a preference for overnight charging over opportunity charging. The results of the FRAFSI are in line with other methods (fuzzy TOPSIS and fuzzy EDAS). Our findings offer valuable insights and guidance for transportation authorities and decision-makers in charge of selecting charging strategies for EBs.
AB - Selecting the best type of charging strategy, among a variety of options such as overnight (slow) charging and opportunity (fast) charging methods, is a crucial step in electrifying bus networks. This step has gained increased importance with the growing need for sustainable transportation solutions and the widespread adoption of electric buses (EBs) in public transport systems. This study develops a multi-criteria decision-making (MCDM) approach for selecting the best EB charging strategy, considering a comprehensive range of criteria, including economic, environmental, social, operational, and quality-of-service criteria. A thorough literature review and a survey of EB experts are conducted to identify key decision-making factors in this area. A Fuzzy Best-Worst Method (FBWM) is designed to determine the weight of criteria, and a Fuzzy Ranking of Alternatives through Functional mapping of criterion subintervals into a Single Interval (FRAFSI) method is designed to rank available charging strategies for EB systems in Munich, Germany. Results show that the most crucial consideration for decision-makers is the economic aspect, followed by operational and environmental factors. The infrastructure cost is the most crucial factor in the economic category, followed by battery cost and operational cost. The driving range is the most crucial factor in the operational category, followed by charging duration and energy monitoring. The experts’ assessments indicate a preference for overnight charging over opportunity charging. The results of the FRAFSI are in line with other methods (fuzzy TOPSIS and fuzzy EDAS). Our findings offer valuable insights and guidance for transportation authorities and decision-makers in charge of selecting charging strategies for EBs.
KW - Charging strategy
KW - Electric bus
KW - Fuzzy best worst method
KW - Multi-criteria decision making
KW - Sustainable transport
UR - http://www.scopus.com/inward/record.url?scp=85163214988&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2023.121415
DO - 10.1016/j.apenergy.2023.121415
M3 - Article
AN - SCOPUS:85163214988
SN - 0306-2619
VL - 347
JO - Applied Energy
JF - Applied Energy
M1 - 121415
ER -