Network user equilibrium problems for the mixed battery electric vehicles and gasoline vehicles subject to battery swapping stations and road grade constraints

The environmental impact of battery electric vehicles (BEVs) largely depends on the environmental profile of the national electric power grid that enables their operation. The purpose of this study is to analyze the environmental performance of BEV usage in Korea considering the changes and trajectory of the national power roadmap. We examined the environmental performance using a weighted environmental index, considering eight impact categories. The results showed that the weighted environmental impact of Korea’s national power grid supply would increase overall by 66% from 2015 to 2029 using the plan laid out by the 7th Power Roadmap, and by only 33% from 2017 to 2031 using the 8th Power Roadmap plan. This change reflects the substantial amount of renewables in the more recent power mix plan. In 2016, BEV usage in Korea resulted in emissions reductions of about 37% compared with diesel passenger vehicles, and 41% compared with gasoline vehicles per kilometer driven (100 g CO2e/km versus 158 g and 170 g CO2e/km, respectively) related to transportation sector. By 2030, BEV usage in Korea is expected to achieve a greater emissions reduction of about 53% compared with diesel vehicles and 56% compared with gasoline vehicles. However, trade-offs are also expected because of increased particulate matter (PM) pollution, which we anticipate to increase by 84% compared with 2016 conditions. Despite these projected increases in PM emissions, increased BEV usage in Korea is expected to result in important global and local benefits through reductions of climate-changing greenhouse gas (GHG) emissions.

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Network user equilibrium of battery electric vehicles considering flow-dependent electricity consumption

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2018.07.009 ◽

2018 ◽

Vol 95 ◽

pp. 516-544 ◽

Cited By ~ 6

Author(s):

Zhaocai Liu ◽

Ziqi Song

Keyword(s):

Electric Vehicles ◽

Electricity Consumption ◽

User Equilibrium ◽

Battery Electric Vehicles

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Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211031529 ◽

2021 ◽

pp. 036119812110315

Author(s):

Kyoungho Ahn ◽

Youssef Bichiou ◽

Mohamed Farag ◽

Hesham A. Rakha

Keyword(s):

Energy Consumption ◽

Travel Time ◽

Electric Vehicles ◽

Large Scale ◽

Traffic Assignment ◽

Combustion Engine ◽

Model Development ◽

User Equilibrium ◽

Multi Objective ◽

Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

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Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands

Energies ◽

10.3390/en13081964 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1964

Author(s):

Hong Gao ◽

Kai Liu ◽

Xinchao Peng ◽

Cheng Li

Keyword(s):

Electric Vehicles ◽

Rapid Development ◽

User Equilibrium ◽

Hybrid Network ◽

Mixed Integer ◽

Nonlinear Program ◽

Gasoline Vehicles ◽

Level Model ◽

Elastic Demands ◽

Charging Stations

With the rapid development of electric vehicles (EVs), one of the urgent issues is how to deploy limited charging facilities to provide services for as many EVs as possible. This paper proposes a bilevel model to depict the interaction between traffic flow distribution and the location of charging stations (CSs) in the EVs and gasoline vehicles (GVs) hybrid network. The upper level model is a maximum flow-covering model where the CSs are deployed on links with higher demands. The lower level model is a stochastic user equilibrium model under elastic demands (SUE-ED) that considers both demands uncertainty and perceived path constraints, which have a significant influence on the distribution of link flow. Besides the path travel cost, the utility of charging facilities, charging speed, and waiting time at CSs due to space capacity restraint are also considered for the EVs when making a path assignment in the lower level model. A mixed-integer nonlinear program is constructed, and the equivalence of SUE-ED is proven, where a heuristic algorithm is used to solve the model. Finally, the network trial and sensitivity analysis are carried out to illustrate the feasibility and effectiveness of the proposed model.

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Regional Emissions Analysis of Light-Duty Battery Electric Vehicles

Atmosphere ◽

10.3390/atmos12111482 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1482

Author(s):

Andrew Burnham ◽

Zifeng Lu ◽

Michael Wang ◽

Amgad Elgowainy

Keyword(s):

Electric Vehicles ◽

State Level ◽

Air Pollutant ◽

Regional Variability ◽

Emission Data ◽

Battery Electric Vehicles ◽

Light Duty ◽

Vehicle Operation ◽

Gasoline Vehicles ◽

Light Duty Vehicle

Light-duty battery electric vehicles (BEVs) can reduce both greenhouse gas (GHG) and criteria air pollutant (CAPs) emissions, when compared to gasoline vehicles. However, research has found that while today’s BEVs typically reduce GHGs, they can increase certain CAPs, though with significant regional variability based on the electric grid mix. In addition, the environmental performance of electric and gasoline vehicles is not static, as key factors driving emissions have undergone significant changes recently and are expected to continue to evolve. In this study, we perform a cradle-to-grave life cycle analysis using state-level generation mix and vehicle operation emission data. We generated state-level emission factors using a projection from 2020 to 2050 for three light-duty vehicle types. We found that BEVs currently provide GHG benefits in nearly every state, with the median state’s benefit being between approximately 50% to 60% lower than gasoline counterparts. However, gasoline vehicles currently have lower total NOx, urban NOx, total PM2.5, and urban PM2.5 in 33%; 15%; 70%; and 10% of states, respectively. BEV emissions will decrease in 2050 due to a cleaner grid, but the relative benefits when compared to gasoline vehicles do not change significantly, as gasoline vehicles are also improving over this time.

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Congestion Control for Mixed-Mode Traffic with Emission Cost

Journal of Advanced Transportation ◽

10.1155/2020/8814139 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Ya Li ◽

Renhuai Liu ◽

Yuanyang Zou ◽

Yingshuang Ma ◽

Guoxin Wang

Keyword(s):

Congestion Control ◽

Electric Vehicles ◽

Assignment Problem ◽

Mixed Mode ◽

Numerical Experiments ◽

Battery Electric Vehicles ◽

Operation Costs ◽

Traffic Assignment Problem ◽

Gasoline Vehicles ◽

Distance Limit

This paper presents two models to investigate the traffic assignment problem. In the two models, the emission cost for gasoline vehicles (GVs) is considered. The credit schemes are considered in the constraint of the models. The operation costs for battery electric vehicles (BEVs) and GVs are also studied. Particularly, the constraints related to the credit schemes can be utilized to adjust the number of GVs and to promote growth of the number of BEVs, which is a novel idea that was not studied. Preliminary numerical experiments demonstrate that the models are effective and the extended distance limit of BEVs can raise the volume of BEVs under the condition that the unit traffic cost of BEVs is lower than GVs. Therefore, it is feasible to control the quantity of GVs by adjusting the total credit schemes, and it is viable to reduce the emission by enlarging the number of BEVs’ users.

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