Electric Vehicles Charging: Management and Control Strategies

Soares, Filipe; Rua, David; Gouveia, Clara; Tavares, Bruna; Coelho, António Manuel Flores Romão de Azevedo Gonçalves; Lopes, João

doi:10.1109/mvt.2017.2781538

Cited by 28 publications

(20 citation statements)

References 11 publications

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“…The core function of an energy-management system is balancing the power distribution among multiple onboard energy/power sources, with the goal of optimizing some cost functions, such as fuel consumption, battery life, pollution emissions, and driving mobility. This issue is usually formulated as an optimal control problem that has desired control objectives and particular physical constraints [10]. The control objectives may contain one or several options ranging from exhaust temperature, nitrogen oxide and sulfur oxides emissions, fuel consumption, shift frequency, the battery's state of charge (SOC) and state of health (SOH), and the cost of electricity.…”

Section: The Energy-management Problemmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

199

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E nergy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field.

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Section: The Energy-management Problemmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

199

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“…b. Utility function (10) is continuous and quasi-concave for strategy set (3) and (4). Proof (a): In strategy set (3) and (4), the constraint conditions are linear inequalities.…”

Section: A Nash Equilibrium Of Complete Gamementioning

confidence: 99%

“…The system not only integrates the advantages of EVs and MGs, but also initiates the newly developed research fields of MGs [2]. Moreover, compared to conventional hybrid electric vehicles (HEVs), evolved EVs, such as plug-in electric vehicles (PEVs), have enlarged battery capacities and bidirectional converters thus they can not only charge but also discharge their batteries [3].…”

Section: Introductionmentioning

confidence: 99%

A Game Theoretical Pricing Mechanism for Multi-Microgrid Energy Trading Considering Electric Vehicles Uncertainty

2020

IEEE Access

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The electricity price mechanism based on game theory is one of the research focuses on microgrids energy trading. The complete information game is based on the certainty of the identity of roles of players and variables. However, there are many uncertain factors that cause the game in the state of incomplete information. In this paper, Microgrids Energy Trading Bayesian Game (METBG) model is proposed. The model was based on the Bayesian game, in which MGs make their decision as an agent of native users to tackle bidirectional energy trading between others. First, The Bayesian game modeled types of roles of players by the uncertainty of information including the stochastic characteristics of PEVs which result in hardness that the game participants determine whether they are sellers or buyers in the utility function that depends on the state of power surplus or lack. Moreover, the utility model of players was established by a Bayesian game with the game equilibrium derived rigorously by obligated to coordinate the sharing of energy with maximization of the players' profit. Finally, the solution of game equilibrium has been rigorously derived, and the effectiveness of the model is verified in terms of seller profit, the utilities of buyers, and the net energy usage in the microgrids. The results of the static pricing model and proposed model were compared to demonstrate the effectiveness. INDEX TERMS Multi-microgrid; Electric vehicles; Energy trading; Bayesian game

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“…Unlike many others, this study improved the state-of-the-art in experimental testing. The work in [26] introduced a charging strategy with similar objectives as the one in [25], considering voltage and thermal limits of the network based on droop control. In addition to positive results, the authors discussed the limitations of the communications, which is a crucial aspect in commercial implementations.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations

et al. 2020

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The global proliferation of plug-in electric vehicles (PEVs) poses a major challenge for current and future distribution systems. If uncoordinated, their charging process may cause congestion on both network transformers and feeders, resulting in overheating, deterioration, protection triggering and eventual risk of failure, seriously compromising the stability and reliability of the grid. To mitigate such impacts and increase their hosting capacity in radial distribution systems, the present study compares the levels of effectiveness and performances of three alternative centralized thermal management formulations for a real-time agent-based charge control algorithm that aims to minimize the total impact upon car owners. A linear formulation and a convex formulation of the optimization problem are presented and solved respectively by means of integer linear programming and a genetic algorithm. The obtained results are then compared, in terms of their total impact on the end-users and overall performance, with those of the current heuristic implementation of the algorithm. All implementations were tested using a simulation environment considering multiple vehicle penetration and base load levels, and equipment modeled after commercially available charging stations and vehicles. Results show how faster resolution times are achieved by the heuristic implementation, but no significant differences between formulations exist in terms of network management and end-user impact. Every vehicle reached its maximum charge level while all thermal impacts were mitigated for all considered scenarios. The most demanding scenario showcased over a 30% reduction in the peak load for all thermal variants.

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Electric Vehicles Charging: Management and Control Strategies

Cited by 28 publications

References 11 publications

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

A Game Theoretical Pricing Mechanism for Multi-Microgrid Energy Trading Considering Electric Vehicles Uncertainty

Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations

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