TY - JOUR
T1 - A data-driven multi-objective optimization framework for determining the suitability of hydrogen fuel cell vehicles in freight transport
AU - Wang, Shiqi
AU - Peng, Zhenhan
AU - Wang, Pinxi
AU - Chen, Anthony
AU - Zhuge, Chengxiang
N1 - Funding Information:
The work described in this paper was jointly supported by the National Natural Science Foundation of China (52002345), the research grants from the Research Institute for Sustainable Urban Development (1-BBWF and 1-BBWR), the Smart Cities Research Institute (CDAR and CDA9) and the funding for Project of Strategic Importance provided by The Hong Kong Polytechnic University (1-ZE0A).
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10/1
Y1 - 2023/10/1
N2 - In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles.
AB - In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles.
KW - Battery electric vehicle
KW - Data-driven simulation
KW - Freight transport system
KW - Hydrogen fuel cell vehicle
KW - Life cycle analysis
UR - http://www.scopus.com/inward/record.url?scp=85164513559&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2023.121452
DO - 10.1016/j.apenergy.2023.121452
M3 - Journal article
AN - SCOPUS:85164513559
SN - 0306-2619
VL - 347
JO - Applied Energy
JF - Applied Energy
M1 - 121452
ER -