TY - JOUR
T1 - Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet
AU - Fraile-Ardanuy, Jesús
AU - Castano-Solis, Sandra
AU - Álvaro-Hermana, Roberto
AU - Merino, Julia
AU - Castillo, Ángela
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles.
AB - Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles.
KW - Big data
KW - Mobility pattern
KW - GPS data
KW - Electric vehicle
KW - Charging stations
KW - Spatial-temporal electricity demand
KW - Big data
KW - Mobility pattern
KW - GPS data
KW - Electric vehicle
KW - Charging stations
KW - Spatial-temporal electricity demand
UR - http://www.scopus.com/inward/record.url?scp=85040773573&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2017.11.070
DO - 10.1016/j.enconman.2017.11.070
M3 - Article
SN - 0196-8904
VL - 157
SP - 59
EP - 70
JO - Energy Conversion and Management
JF - Energy Conversion and Management
ER -
