Optimization models for electric vehicle service operations: A literature review

Shen, Zuo‐Jun Max; Feng, Bo; Mao, Chao; Ran, Lun

doi:10.1016/j.trb.2019.08.006

Cited by 181 publications

(65 citation statements)

References 86 publications

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“…Although there exist several review works in the literature with regards to EV charging, they do not focus on the charging behavior from a data driven approach. For instance, [28] provides a survey of optimization and mathematical modelling-based solutions to EV operations and management, including EV charging. Aqueel et al [29] provided a review of various charging technologies and standards with a case study from Germany.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…In recent literature, there are several papers related to EVs' charging and optimization. In (Shen et al 2019), a survey is presented on EVs operations management. Different models and methods have been considered for the following topics: location of charging stations, charging operations (considering centralized and decentralized approaches), business models, and policy.…”

Section: State Of the Artmentioning

confidence: 99%

Discrete event optimization of a vehicle charging station with multiple sockets

Ferro

Minciardi

Parodi

et al. 2020

Discrete Event Dyn Syst

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The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

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“…Although there exist several review papers with regards to EV charging, they do not focus on charging coordination from a data-driven approach, specifically with the application of learning-based coordination. For instance, the review in [29] classifies the literature in EV charging to infrastructure planning, charging operations, public policy, and business models. Various technologies and standards for EV charging is reviewed in [30] as a case study from Germany.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based EV Charging Management Systems–A Review

2021

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To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This paper reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This paper also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.

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Optimization models for electric vehicle service operations: A literature review

Cited by 181 publications

References 86 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Discrete event optimization of a vehicle charging station with multiple sockets

Reinforcement Learning Based EV Charging Management Systems–A Review

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