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Electric vehicle charging strategy study and the application on charging station placement

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Abstract

Optimal placement of charging stations for electric vehicles (EVs) is critical for providing convenient charging service to EV owners and promoting public acceptance of EVs. There has been a lot of work on EV charging station placement, yet EV drivers’ charging strategy, which plays an important role in deciding charging stations’ performance, is missing. EV drivers make choice among charging stations according to various factors, including the distance, the charging fare and queuing condition in different stations etc. In turn, some factors, like queuing condition, is greatly influenced by EV drivers’ choices. As more EVs visit the same station, longer queuing duration should be expected. This work first proposes a behavior model to capture the decision making of EV drivers in choosing charging stations, based on which an optimal charging station placement model is presented to minimize the social cost (defined as the congestion in charging stations suffered by all EV drivers). Through analyzing EV drivers’ decision-making in the charging process, we propose a k-Level nested Quantal Response Equilibrium charging behavior model inspired by Quantal Response Equilibrium model and level-k thinking model. We then design a set of user studies to simulate charging scenarios and collect data from human players to learn the parameters of different behavior models. Experimental results show that our charging behavior model can better capture the bounded rationality of human players in the charging activity compared with state-of-the-art behavior models. Furthermore, to evaluate the proposed charging behavior model, we formulate the charging station placement problem with it and design an algorithm to solve the problem. It is shown that our approach obtains placement with a significantly better performance to different extent, especially when the budget is limited and relatively low.

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Notes

  1. This is inspired by the city plan of Singapore (http://www.propertyhub.com.sg/singapore-district-guide.html). Based on the zoning assumption, residents in the same zone are living relatively close. Although identifying the specific location and treating each of them as a different player would be closer to the real-world scenario, it’s relatively unrealistic for formulating and solving the optimization problem. Thus, we make the comprise and treat them as a group of identical agents.

  2. Readers might wonder why only one charging station is considered in one zone. The reason is that in case there are multiple charging stations in one zone, we can always divide the zone into a number of new zones, each with one charging station.

  3. Under some circumstances, EV drivers might have different benefits while charging their EVs in different charging stations (e.g., getting access to other facilities). In that case, our model can be extended by deducting the benefit in the cost function.

  4. We list the most common factors that influence EV drivers’ charging cost. While other factors may make a difference in some special scenarios, the model can be extended accordingly.

  5. Linear combination is a commonly used simple but powerful method.

  6. The reason is that we think the most important factor is the congestion in charging station for this placement problem. But our framework is able to be extended to include other factors.

  7. The design of the game has been performed in several iterations with studies on human players to ensure that they are aware of all the games’ parameters.

  8. We design the charging games to capture the players’ behavior when competition exists in the charging scenario by setting one start point but multiple EV drivers. Although there are EV drivers from other start points in real-world scenarios, they form the equilibrium after repeating charging activities in a long term. Setting a too complicated charging game would make it unrealistic for players to fit in a short time.

  9. In real-world application, a charging station can be located in other positions. In the experiments, we say that a charging station is next to a shopping center to provide a simulated scenario for players.

  10. Parameters are available on https://drive.google.com/open?id=1K6AYYA_vq6NjYm1jJtSSjcsENwV1ZqdX

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Authors and Affiliations

  1. Dartmouth College, Hanover, NH, USA

    Yanhai Xiong

  2. Nanyang Technological University, Jurong West, Singapore

    Bo An

  3. Bar-Ilan University, Ramat Gan, Israel

    Sarit Kraus

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  1. Yanhai Xiong
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  2. Bo An
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Correspondence to Yanhai Xiong.

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Xiong, Y., An, B. & Kraus, S. Electric vehicle charging strategy study and the application on charging station placement. Auton Agent Multi-Agent Syst 35, 3 (2021). https://doi.org/10.1007/s10458-020-09484-5

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