Article
Management
Li Chen, Long He, Yangfang (Helen) Zhou
Summary: To support the rapid growth in global electric vehicle adoption, public charging of electric vehicles is crucial. This study focuses on the problem faced by an electric vehicle charging service provider, including stochastic arrival of customers and a total electricity cost. The problem is formulated as a stochastic program and solved using exponential cone program approximations. The approach is benchmarked against other methods and found to be more efficient and cost-effective. The study also extends the approach to include pricing decisions and provides managerial insights for charging service providers and policymakers.
OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Xi Chen, Haihui Wang, Fan Wu, Yujie Wu, Marta C. Gonzalez, Junshan Zhang
Summary: This article presents a model of the electric vehicle (EV) charging network as a cyber-physical system that is coupled with transportation networks and smart grids. An EV charging station recommendation algorithm is proposed to create synergy between transportation networks and smart grids and utilize EV charging activities as a load-balancing tool to transfer energy between unbalanced distribution grids.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Energy & Fuels
Zexin Yang, Xueliang Huang, Tian Gao, Qi Zhao, Hongen Ding
Summary: A distributed collaborative control strategy based on consensus algorithm is proposed in this paper to control the operation of multiple charging stations, considering the difficulty of centralized optimization control strategy in adapting to the access of large-scale electric vehicles. By establishing a charging station aggregation model, the optimization variables of large-scale electric vehicle access are reduced, and power allocation among charging stations is realized while satisfying global and local constraints. Case analysis proves the accuracy, effectiveness, and robustness of the method, as well as its adaptability to large-scale electric vehicle access.
Article
Automation & Control Systems
Chenyuan He, Zhouyu Zhang
Summary: This article proposes a hierarchical EV charging control strategy considering various factors. The centralized control aims to minimize the total energy cost, while the distributed control ensures data privacy protection and finds the Nash equilibrium for EVs within an aggregator. Extensive simulations show the effectiveness of the proposed algorithm.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Mostafa Mahfouz, Reza Iravani
Summary: This article presents a supervisory controller for operating an electric vehicle fast charging station in autonomous mode when the supply grid is unavailable. The controller is based on the supervisory control theory and ensures seamless transition between different modes of operation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Teng Long, Qing-Shan Jia, Gongming Wang, Yu Yang
Summary: This paper presents an efficient and scalable real-time scheduling method for handling the charging demands of plug-in electric vehicles (PEV), demonstrating through simulations that the proposed method provides high computation efficiency and scalability while reducing operating costs for charging stations. Compared to existing methods, it outperforms in terms of charging policy search capabilities and performance guarantee.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Thermodynamics
Peter Makeen, Hani A. Ghali, Saim Memon, Fang Duan
Summary: This paper proposes a novel smart techno-economic operation method for electric vehicle charging stations (EVCS) controlled by an aggregator based on a hierarchal model. The deterministic charging scheduling is used to balance the generated and consumed power and surplus power is supplied to the utility grid. Mixed-integer linear programming (MILP) is used to solve the first stage, and Markov Decision Process Reinforcement Learning (MDP-RL) is used to maximize the charging station profit. The outcomes show a sufficient techno-economic hierarchical model for normal operation.
Article
Engineering, Civil
Canqi Yao, Shibo Chen, Zaiyue Yang
Summary: This paper proposes an efficient two-stage algorithm that decomposes the original MIP problem into two LP problems, achieving near-optimal solution in polynomial time by exploiting the exactness of LP relaxation and eliminating the coupled term. Additionally, a variant algorithm based on the two-stage one is introduced to further improve the quality of the solution. Extensive simulations validate the effectiveness of the proposed algorithm compared to state-of-the-art methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Jangkyum Kim, Hyeontaek Oh
Summary: This article proposes an efficient power operation scheme for EV charging facility by jointly analyzing various power rate policies, battery wear-out costs, and uncertainty issues caused by EV users' behavior. The effectiveness of the proposed scheme is analyzed using real-world EV operation datasets, showing a reduction of 20.5% in overall cost and 17.3% in peak power compared to other benchmark models when applied in an actual power system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Energy & Fuels
Jiang Tian, Yang Lv, Qi Zhao, Yucheng Gong, Chun Li, Hongen Ding, Yu Yu
Summary: This article proposes a method for predicting the orderly charging load of electric vehicles based on the characteristics of EV charging behavior and nonlinear programming. It utilizes the Monte Carlo algorithm to predict the charging probability and disorderly charging load of EVs, and employs a nonlinear programming algorithm to solve the optimized target function of orderly charging, thereby achieving the prediction of orderly charging load in the region.
Article
Engineering, Civil
Shuai Mao, Yan Wang, Quanxue Guan, Yunjian Xu
Summary: This study addresses the joint battery charging and replenishment scheduling problem in a battery swapping charging system, taking into account random electric vehicle arrivals, renewable generation, and electricity prices. The proposed approach integrates structural properties, such as threshold-charging and least demand first structures, to reduce the dimensionality of the action space. Experimental results demonstrate that the proposed approach outperforms various structural charging and replenishment policies as well as a vanilla soft actor-critic algorithm, achieving significant cost savings of 7.16%-78.61% and 6.53%-93.73%.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Hojun Jin, Sangkeum Lee, Sarvar Hussain Nengroo, Dongsoo Har
Summary: This study proposes a power management scheme for interdependent microgrid and electric vehicle fleets, assisted by a novel charging/discharging scheduling algorithm. The scheme maximizes the utilization of electric vehicle charging/discharging while minimizing operating costs through multi-objective optimization. It also establishes a more economical and energy-efficient PV-based charging station.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Toni Simolin, Kalle Rauma, Antti Rautiainen, Pertti Jarventausta
Summary: This paper investigates two methods, phase reconfiguration and phase-specific control, to address congestion issues in charging sites. Extensive simulations show that these methods have the potential to increase charging energy and improve the quality of charging service, particularly in highly congested sites. The results also indicate the greater benefits of phase-specific control in congested sites, and discourage the use of perfectly balanced three-phase loading assumption in future studies.
Article
Transportation Science & Technology
Ehsan Mahyari, Nickolas Freeman, Mesut Yavuz
Summary: The past decade has seen a growing interest in transportation electrification from academia, government, and industry. Fleet operators are currently facing a challenge in charge scheduling due to fleet size, heterogeneity, and uncertainty, leading to the emergence of the Charging-as-a-Service (CaaS) industry. This paper addresses the CaaS providers' electric vehicle fleet (EVF) charge scheduling problem and proposes an optimization approach that outperforms industry benchmarks in terms of charging costs and energy consumption.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Engineering, Electrical & Electronic
Jiayan Liu, Gang Lin, Sunhua Huang, Yang Zhou, Yong Li, Christian Rehtanz
Summary: This article proposes an optimal charging scheduling method for electric vehicles in the context of limited charging facilities, aiming to reduce costs and meet the charging demands of each EV by responding to time-of-use electricity pricing. The method is formulated as a bilevel programming model and is shown to be effective in simulation results.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
