Locating Battery Swapping Stations for a Smart e-Bus System

Moon, Joon; Kim, Young Joo; Cheong, Taesu; Song, Sang Hwa

doi:10.3390/su12031142

Cited by 21 publications

(10 citation statements)

References 22 publications

(19 reference statements)

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“…Assumptions: (a) Due to battery aging, it is assumed that BEV battery's life is 12 years, since the battery will have lost a significant part of its capacity after this period; this is an acceptable assumption since published data on battery lifespan ranges from 8 to 15 years [29][30][31]. Battery swapping models are complex and currently not foreseen for passenger vehicles (although they could be viable for electric buses in public transportation [32]), so it is assumed the vehicle needs to be scrapped after the 12 years lifetime, even if the maximal life cycle mileage has not been achieved. (b) Therefore, for each data point, LCA calculations are based on the life cycle maximal mileage, if achieved.…”

Section: Methods: Analysis Of the Environmentally Optimal Drivetrain MIXmentioning

confidence: 99%

Automotive Electrification Challenges Shown by Real-World Driving Data and Lifecycle Assessment

et al. 2022

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Electric mobility is considered a solution to reduce carbon emissions. We expanded a lifecycle assessment with data on technical limitations and driving habits (based on real-world data) in order to identify the environmentally optimal drivetrain for each individual driving behavior with current and projected technologies, focusing on CO2 emissions. By combining all data, an environmentally optimal European drivetrain mix is calculated, which is dominated by fuel-cell electric vehicles (50% in 2020, 47% in 2030), followed by plug-in hybrid-electric vehicles (37%, 40%), battery-electric vehicles (BEV) (5%, 12%), and Diesel vehicles (2%, 1%). Driving behavior defines the most environmental drivetrain and the coexistence of different drivetrains is currently still necessary. Such information is crucial to identify limitations and unmet technological needs for full electrification. If range is not considered a limitation, the environmentally optimal drivetrain mix is dominated by BEVs (71%, 75%), followed by fuel cell electric vehicles (25%, 19%) and plug-in electric vehicles (4%, 6%). This confirms the potential environmental benefits of BEVs for current and future transportation. Developments in battery energy density, charging, and sustainable production, as well as a change in driving behavior, will be crucial to make BEVs the environmentally optimal drivetrain choice.

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Section: Methods: Analysis Of the Environmentally Optimal Drivetrain MIXmentioning

confidence: 99%

Automotive Electrification Challenges Shown by Real-World Driving Data and Lifecycle Assessment

et al. 2022

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“…Bus studies focused on determining charging facility location [103,163,166,167,170,184,189,198,200] and capacity [163], routes [163,200], bus scheduling [80,155,166,167], fleet sizing [163], vehicle design [103,163] and fleet charging schedule [108,165,180]. Regarding energy planning, Ifaei et al [115] aimed at determining the best option of photovoltaic (PV) panels to be implemented on the roof of buses for the electrification of public transport, and Elkamel et al [105] at determining the schedules of power-generating units in order to supply an electric mobility system.…”

Section: Technology and Energymentioning

confidence: 99%

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

Borchers,

Wittowsky,

Augusto Souza Fernandes

2024

IEEE Access

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Climate change is currently the biggest environmental threat, being the cities responsible for a significant impact on greenhouse gas emissions. In this sense, the transport sector is among the main causes for both emissions and the depletion of non-renewable resources. Considering this scenario, there is an appeal to build more accessible, smart, sustainable and energy-efficient cities, promoting energy transition in urban systems and increasing the quality of urban life. This review aims to analyse transport and urban mobility studies that employ optimisation techniques to achieve environmental, sustainability or climate change mitigation goals. After an overview regarding modelling aspects of how such goals were addressed, the nature of the objective functions, the perspectives considered, and the network application, such studies were classified into five areas and further detailed. The areas comprise: (i) planning and policy-making; (ii) environmental variables; (iii) demand and traffic management; (iv) technology and energy; and (v) nonspatial measures. In this sense, future research directions should include optimisation models that consider the social aspects of transport and the interests of passengers, operators and the community simultaneously, improve the modelling of environmental impacts to increase its robustness, and deal with large network problems.INDEX TERMS Climate change, Greenhouse gas (GHG) emissions, Optimisation, Passenger urban transport, Sustainable mobility.

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“…By minimizing the net losses of a distribution system, they propose a model for optimal locations of BSSs. Moon et al (2020) propose a mathematical programming model for the deployment of e-bus network switching stations [39]. Their purpose is to determine the location of battery swapping stations and the scheduling problem.…”

Section: Location Optimization Of Battery Swapping Stationsmentioning

confidence: 99%

Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

2020

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In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

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Locating Battery Swapping Stations for a Smart e-Bus System

Cited by 21 publications

References 22 publications

Automotive Electrification Challenges Shown by Real-World Driving Data and Lifecycle Assessment

Automotive Electrification Challenges Shown by Real-World Driving Data and Lifecycle Assessment

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

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