Charging Time and Location Recommendation Strategy Considering Taxi User Satisfaction

Zhang, Jing; Li, Taoyong; Pan, Aiqiang; Long, Xuemei; Jiang, Linru; Liu, Zhe; Zhang, Yuanxing

doi:10.1109/aeees48850.2020.9121420

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…While designing an optimal algorithm to distribute CSs, other supplementary social and construction factors, such as investment costs, maintenance and operating expenditures, rural or urban areas placement, population density, etc., should also be considered to be able to prove that the introduced model is effective and also feasible to implement [39]. For instance, to address the need to establish service area CSs on the route of suburbs of cities, the combination of Asymmetric Nash Negotiation and Hybrid Binary Particle Swarm Optimization algorithms can determine regions' EVs charging demands and CSs service range conditions [40].…”

Section: Charging Stations Distributionmentioning

confidence: 99%

“…One primary metric for e-taxis is to locate a fast-charging station in the nearest location considering the profit maximization, especially during rush hours [11,54]. Zhang et al [39] proposed a recommendation strategy to assign e-taxis the best charging location at the best time. For the charging-time modeling, they computed factors such as e-taxi unit time revenue, charging capacity, charging process duration, and time-of-use electricity cost.…”

Section: Electric Taxismentioning

confidence: 99%

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A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Teimoori

Yassine

2022

Sustainability

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Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. In recent years, privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. Therefore, in this article, a comprehensive literature review is proposed to analyze existing EVs energy management challenges and solutions and present potential future research directions for EVs charging/discharging coordination applications.

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Section: Charging Stations Distributionmentioning

confidence: 99%

Section: Electric Taxismentioning

confidence: 99%

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Teimoori

Yassine

2022

Sustainability

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“…A wide range of factors would be needed to consider the introduction of EV station recommendation systems, such as the battery capacity of the EV, charging time needed, availability of the charging stations, and estimated time to reach the destination. These systems could also incorporate real-time information, such as traffic congestion, weather conditions, and charging station availability [14,15]. In addition to improving the convenience and efficiency of EV charging, the station recommendation systems could help to reduce range anxiety, which would be a common concern among EV drivers who fear they would run out of charge before reaching their destination.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Suanpang,

Jamjuntr

2024

WEVJ

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As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development.

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An Electric Vehicle Cluster Model Considering Multiple Uncertainties and Response Costs

Wang

Yuan

et al. 2022

2022 IEEE 5th International Electrical and Energy Conference (CIEEC)

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Charging Time and Location Recommendation Strategy Considering Taxi User Satisfaction

Cited by 5 publications

References 1 publication

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

An Electric Vehicle Cluster Model Considering Multiple Uncertainties and Response Costs

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