Optimal planning of swapping/charging station network with customer satisfaction

Yang, Jun; Guo, Fang; Min, Zhang

doi:10.1016/j.tre.2017.04.012

Cited by 95 publications

(39 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, with a given reasonable BSEV scale, the geographical distribution problem is mostly considered to deal with the BS demand of BSSs [33], [36]. Some researches focus on the location-routing problems between BSSs and BSEVs to satisfy BS demand as much as possible via optimization algorithms [33]- [35].…”

Section: Ioc Class Coefficient Of Investment and Operation Cost MI Clmentioning

confidence: 99%

“…Some researches focus on the location-routing problems between BSSs and BSEVs to satisfy BS demand as much as possible via optimization algorithms [33]- [35]. Besides, the customers' behavior and psychology are considered to evaluate customers' satisfaction with purchasing EVs [36]. Considering the high investment and a small BSEV scale at present, the accurate data of geographical distribution of traffic flow will contribute to estimate the BS demand of BSSs and optimize the location of BSSs.…”

Section: Ioc Class Coefficient Of Investment and Operation Cost MI Clmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Differential Evolution Algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale Evolution

Wang

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Scientific scale forecasting of multi-type electric vehicles (EVs) is critical to accurately analyze the planning and operation of battery-swapping stations (BSSs) and charging stations (CSs). This paper predicts the proportions of plug-in electric vehicles (PEVs), hybrid electric vehicles (HEVs), and battery-swapping electric vehicles (BSEVs) in the total EV fleet in multi-scenarios via a system dynamics (SD) method. Relying on the predicted evolution scale of the BSEVs and the service demand of BSSs calculated by the service radius (SR) method, an improved differential evolutional algorithm combing with Monte Carlo searching (IDEA-MCS) method is proposed to obtain the optimal location of BSSs in a certain region in Beijing, which achieves an economic optimum of BSSs under the battery-swapping mode (BSM) via centralized charging and unified distribution (CCAUD). The analytical results show that the proportion of the BSEVs in different scenarios is the major driver that impacts the location of BSSs. The distribution of BSSs' BS demand in the optimistic scenario is more inhomogeneous than that in the other scenarios. In addition, a cross-comparison of optimal profits in different scenarios is conducted to verify the optimality of BSS locations for a given scenario. Finally, the proposed IDEA-MCS method is compared with the DEA method and IDEA method to verify its optimality.

show abstract

Section: Ioc Class Coefficient Of Investment and Operation Cost MI Clmentioning

confidence: 99%

Section: Ioc Class Coefficient Of Investment and Operation Cost MI Clmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale Evolution

Wang

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…[1]) and to be easy to develop and implement. Moreover, such a heuristic method was successfully used in [24] for locating EV battery swapping stations.…”

Section: Tabu Search Heuristic Approach For the Stochastic Problemmentioning

confidence: 99%

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Boujelben

Gicquel

2019

Computers & Operations Research

View full text Add to dashboard Cite

We seek to determine the best locations for electric vehicle fast charging stations under driving range uncertainty. Two stochastic programming based models have been recently proposed to handle the resulting stochastic flow refueling location problem: a first one maximizing the expected flow coverage of the network, a second one based on joint chance constraints. However, significant computational difficulties were encountered while solving large-size instances. We thus propose two efficient solution approaches for this problem. The first one is based on a new location-allocation type model for this problem and results in a MILP formulation, while the second one is a tabu search heuristic. Our numerical experiments show that when using the proposed MILP formulation, the computation time needed to provide guaranteed optimal solutions is significantly reduced as compared to the one needed when using the previously published MILP formulation. Moreover, our results also show that the tabu search method consistently provides good quality solutions within short computation times.

show abstract

“…To solve the problems above, an increasing number of researchers have focused on deploying EV charging systems, which will significantly shape current EV coverage [10,14]. These infrastructures can generally be divided into three categories [15]: plug-in EVs (PEVs, i.e., slow chargers and fast chargers) [16], wireless charging EVs (WCEVs, i.e., inductive charging during driving) [17,18], and swap charging EVs (SCEVs) [19,20]. Table 1 shows the comparisons between these infrastructures.…”

Section: Introductionmentioning

confidence: 99%

“…The SSCS has a few new features, as listed below: Swap charging (SC) can reduce the peak consumption of electricity by centralized charging [30,31] and avoid grid overloading due to mass EV charging [32] because the empty batteries that are swapped out can be charged when electricity is cheap or demand is low. Since SCEVs are considered to be a suitable EV mode, an increasing number of studies on the SC system (SCS) have emerged worldwide [4,19,25,27,33]. The Fluence Z.E.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design for a Shared Swap Charging System Considering the Electric Vehicle Battery Charging Rate

Zhong

Pei

2020

Energies

View full text Add to dashboard Cite

Swap charging (SC) technology offers the possibility of swapping the batteries of electric vehicles (EVs), providing a perfect solution for achieving a long-distance freeway trip. Based on SC technology, a shared SC system (SSCS) concept is proposed to overcome the difficulties in optimal swap battery strategies for a large number of EVs with charging requests and to consider the variance in the battery charging rate simultaneously. To realize the optimal SSCS design, a binary integer programming model is developed to balance the tradeoff between the detour travel cost and the total battery recharge cost in the SSCS. The proposed method is verified with a numerical example of the freeway system in Guangdong Province, China, and can obtain an exact solution using off-the-shelf commercial solvers (e.g., Gurobi).

show abstract

Optimal planning of swapping/charging station network with customer satisfaction

Cited by 95 publications

References 47 publications

An Improved Differential Evolution Algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale Evolution

An Improved Differential Evolution Algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale Evolution

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Optimal Design for a Shared Swap Charging System Considering the Electric Vehicle Battery Charging Rate

Contact Info

Product

Resources

About