Optimized time step for electric vehicle charging optimization considering cost and temperature

Dahmane, Yassir; Chenouard, Raphaël; Ghanes, Malek; Alvarado-Ruiz, Mario

doi:10.1016/j.segan.2021.100468

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…Thus, the time interval for the EV simulations should be determined by an advanced analysis framework using real driving data. The impacts of V2G on the power grid can give significantly better results in terms of technical, economic and environmental if real EV behavior-based time interval is used [73]. We have compared the technical result of commonly used time intervals in V2G impact analyses.…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

et al. 2023

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Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects. INDEX TERMSBootstrap, charging behavior, distributed network, driving data, electric vehicle. ALPASLAN DEMIRCI received the B.S. and M.S. degrees in electrical engineering from Marmara University, Istanbul, Turkey, in 2007 and 2011, respectively, and the second B.S. degree in electrical and electronics engineering from Sakarya University, Turkey, in 2017. He is currently pursuing the Ph.D. degree in electrical engineering with Yıldız Technical University (YTU), Istanbul. From 2012 to 2016, he was a Lecturer at the Department of Electricity and Energy, YTU, where he is a Lecturer with the Electrical Engineering Department, since 2016. His research interests include power system optimization methods, distributed renewable energy systems, and energy economics and electric vehicles. He has published several papers related to his research areas.

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Section: Resultsmentioning

confidence: 99%

A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

et al. 2023

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“…As delivery vehicle routes are predictable, schedulable, and repeatable (Tsakalidis et al., 2020), delivery vehicles are well suited to time‐of‐day charging optimization (Dahmane et al., 2021). We use a multiobjective optimization to select charging times that minimize emissions and/or costs (see Table S5 for optimization nomenclature).…”

Section: Methodsmentioning

confidence: 99%

Optimizing future cost and emissions of electric delivery vehicles

Woody

Craig

Vaishnav

et al. 2022

J of Industrial Ecology

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Electrification of delivery vehicles will play an important role in decarbonizing the transportation sector. As electricity-generating technologies vary regionally and temporally, where electric vehicles are deployed and when they are charged will determine the greenhouse gas (GHG) emissions and cost consequences of delivery vehicle electrification. We couple a vehicle charging model with a dataset that provides hourly projections of marginal electricity cost and marginal emissions factors across 134 electricity balancing areas in the United States. We calculate the cost and emissions of charging an electric delivery vehicle over a 10-year service life (2021-2030) at different times of day and in different locations. Using a multiobjective optimization framework, we explore two potential goals-minimizing GHG emissions and minimizing cost-and investigate the tradeoffs between those goals. We show emissions ranging from 136 to 485 g CO 2 /mile, and costs ranging from 0.79 to 3.18 cents/mile depending on location and optimization weighting. We demonstrate the impact of charge time-of-day optimization frequency, showing emissions reductions of 19-62% by choosing the optimal charging time every day, rather than annually. We show that the benefits of electrification are reduced when potential charge times are constrained (e.g., if charging must take place overnight), and we calculate the carbon price needed to align cost-optimized and emissions-optimized charge timing in different regions.Our results highlight the opportunity to reduce cost and emissions by strategically charging at certain times of day and show the importance of accounting for spatial and temporal variability when developing effective carbon-reduction strategies.

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“…Temperature, short circuit, fire impact, vibration, humidity, and water immersion are just a few of the conditions that the batteries must survive. Not only the above safety requirements but also collision detection, short circuit detection, and insulation should all be included in high-voltage transmission lines [64].…”

Section: H Ev Security Necessitiesmentioning

confidence: 99%

Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

et al. 2022

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Transportation is currently advancing towards Electric Vehicle (EV) technology. This paper presents a brief and systematic analysis of the real-time issues obtained in Electric Vehicles (EVs) due to the various ranges of energy storage devices. In general, EV energy management system is integrated with power electronic circuits for effective power conversion and reliable operation. Some issues are addressed while using the batteries in EV systems such as charging time, efficiency of battery, and raw materials. Not only battery issues but also real-time non-technical issues and operational issues are also discussed in this paper. During energy conversion with power electronic circuits, the system attains extreme temperature levels which in turn reduces the performance of the system. To maintain an optimum temperature level, it is important to study the temperature effect of the system at the most prior levels. Due to the adaption of power electronic components, some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, to mitigate the EMI effect, the low pass filter-based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply systems in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behaviour of BCS the proposed RNN controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.

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Optimized time step for electric vehicle charging optimization considering cost and temperature

Cited by 16 publications

References 25 publications

A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

Optimizing future cost and emissions of electric delivery vehicles

Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

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