Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Ma, Tai-Yu; Xie, Simin

doi:10.1016/j.trd.2020.102682

Cited by 58 publications

(28 citation statements)

References 38 publications

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“…For this purpose, predicting charging occupancy patterns allows charging service platforms to better manage the limited charging resources available and reduce a customer's charging waiting time loss. For example, with predicted charging waiting times at charging stations, a real-time vehicle-charging station assignment/recommendation system could be developed to reduce the charging waiting time of EV fleets (Tian et al, 2016;Yuan et al, 2019;Ma and Xie, 2021;Ma, 2021). It can also support the development of apps for reducing vehicle idle time when terminating charging sessions (EU Science Hub, 2019) that are directly integrated into the vehicle's user interface (with cellular connectivity) or on the user's smartphone.…”

Section: Introductionmentioning

confidence: 99%

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Faye

2022

Energy

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Section: Introductionmentioning

confidence: 99%

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Faye

2022

Energy

Self Cite

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“…Here, the PSO is implemented as a search strategy. A scheme for the charger allocation considering ride sharing was proposed in [44] considering the minimization of the vehicle idle time as the objective function. The optimization issue was handled by a surrogate-assisted optimization approach.…”

Section: Introductionmentioning

confidence: 99%

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

et al. 2021

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The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

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“…A similar study is conducted in Reference 26, in which the effects of newly deployed EVCSs on EV fleet downtime and travel time are discussed. Ma et al further integrate the real‐time online operation model for electric ridesharing into the fast EVCS planning 27 . In Reference 28, three general modeling principles are employed, including maximizing coverage of EVCSs, minimizing distance between customers and EVCSs, and avoiding EVCS placements in areas with low parking availability, to determine the EVCS planning strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, risk management is adopted in this work. Table 1 provides a summary of comparisons between this work and the previous relevant literatures 23–30 …”

Section: Introductionmentioning

confidence: 99%

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

Wang

Tang

Lai

et al. 2021

Int Trans Electr Energ Syst

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Summary With the increasing market share of electric vehicles (EVs), optimizing the planning decisions of charging stations becomes critical to support long‐distance traveling. Besides, the booming car‐sharing industry provides an alternative business model to enhance the utilization of vehicle resources. Recently, several car‐sharing enterprises have launched the collaborations with charging station construction companies to construct their own charging facilities, which calls for the co‐optimization of charging station planning and scheduling of EVs in car‐sharing business. Therefore, this paper develops an optimization model for electric vehicle charging stations placements and type selections, considering routing selection and charging management of an electric vehicle fleet in car‐sharing business. Specifically, a stochastic programming framework is exploited to incorporate a variety of operating scenarios, which reflect the uncertainties of riders' requirements, drivers' itineraries, traffic flow information, and electricity prices. The proposed model aims to improve the welfare of all riders and reduce total cost of fulfilling riders' pickup and delivery requirements simultaneously, in which determining the optimal locations and types of charging facilities is required. Moreover, portfolio management is integrated into the optimization model to mitigate investment risk. After a series of reformulation, the proposed optimization model can be recast into a mixed‐integer linear programming problem. Using two transportation networks, the effectiveness of the proposed model is validated in numerical studies. It finds that the optimal electric vehicle charging station planning strategy is determined according to the operator's preferences, electricity prices, and installation budgets. Moreover, diversity within operating scenarios yields the positive value‐of‐stochastic‐solution that demonstrates the necessity of implementing stochastic programming.

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Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Cited by 58 publications

References 38 publications

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

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