Electric Bus Charging Station’s Location and Capacity Based on Routes and Grid AP Clustering Algorithm

Fan, Yue; He, Chuan; Fei, Danxiong; Gu, Jiu; Xie, Da

doi:10.1109/aparm49247.2020.9209440

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Therefore, the final number of charging piles is related to night parking time Tci, the number of terminals of each charging pile kci, the time spent by employees to swap charging buses top and the remaining power of bus when it enters charging station SOC0,li,j. The take-turn model can be expressed as (15).…”

Section: ) Improvement Based On the Critic Weighting Methodsmentioning

confidence: 99%

“…In terms of EB charging facility research, there are three main ways to supply electric energy for EBs: battery swapping charging [8][9], wireless charging [10][11], and plug-in charging [12][13][14][15][16][17]. While battery swapping has the advantages of short waiting time and convenient battery management [8][9], the safety issues associated with frequent battery swapping and the difficulty of implementing battery standardization hinder its application in public transport electrification.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Beltran et al [5] and Pternea et al [6] did not consider the influence of EB charging facilities and charging demand. And the studies in [14], [15], [16] only considered fast-charging stations along the route and did not include bus overnight longtime charging at bus stations. In the aspects of systematic transit system electrification method [23]- [26], with minimizing the total cost of ownership of the electric bus fleet, Teicher et al [23] present a method to choose the optimal battery size, number of charging stations and other environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Planning of Regional Urban Bus Charging Facility: A Case Study of Fengxian, Shanghai

Wang

Xie

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The electrification of public transport is of great significance to alleviating environmental pollution and energy problems. The construction of charging stations for electric buses (EBs) is the key step for the electrification of public transport and receives more and more attention. This paper proposes a new urban electric bus charging station planning algorithm which consists of two parts, park-maintaining (PM) charging station planning and midway supply (MS) charging station planning. Firstly, bus routes are classified based on charging demands. Accordingly, the PM charging station planning model is divided into full slow charging (FSC) model, Bus Rapid Transit (BRT) model and Hybrid model. Secondly, the improved grid AP algorithm is applied to plan MS charging stations to enhance the EB operation reliability. Then by multi-terminal charging pile optimization model, the economics of charging facilities construction is enhanced. Finally, via an ordered control charging algorithm, the economic profits of overall planning schemes are enhanced. The bus system in Fengxian, Shanghai is taken as an example to demonstrate the proposed method. Results prove that the proposed method can effectively meet the charging demands of EBs and improve the operating reliability of the EB system.

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Section: ) Improvement Based On the Critic Weighting Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Planning of Regional Urban Bus Charging Facility: A Case Study of Fengxian, Shanghai

Wang

Xie

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Clustering of Photovoltaic Power Stations Based on the Improved AP Clustering Algorithm

Ding,

Gao,

Jiang

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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RFID Indoor Positioning Based on AP Clustering and Improved Particle Swarm Algorithm

Zhang

et al. 2022

Journal of Sensors

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This paper proposes a method based on AP clustering and an improved particle swarm algorithm for radio frequency identification (RFID) indoor positioning, called the AP-PSO method. Firstly, an AP clustering algorithm is used to cluster the RSSI values of the experimental region tags with similarity, in order to achieve the division of tagged regions, reduce the search area of the later improved particle swarm algorithm, and reduce the search time. Secondly, the learning factor of the particle swarm algorithm is dynamically adjusted, in order to improve the search ability and convergence speed of the global optimal solution of particles. The experimental results show that the algorithm can effectively achieve RFID indoor positioning of the tags to be measured, with high positioning accuracy and with the algorithm spending less time.

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Electric Bus Charging Station’s Location and Capacity Based on Routes and Grid AP Clustering Algorithm

Cited by 3 publications

References 9 publications

Planning of Regional Urban Bus Charging Facility: A Case Study of Fengxian, Shanghai

Planning of Regional Urban Bus Charging Facility: A Case Study of Fengxian, Shanghai

Clustering of Photovoltaic Power Stations Based on the Improved AP Clustering Algorithm

RFID Indoor Positioning Based on AP Clustering and Improved Particle Swarm Algorithm

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