Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services

Essiet, Ima O.; Sun, Yuqing

doi:10.1016/j.egyr.2021.07.029

Cited by 22 publications

(5 citation statements)

References 27 publications

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“…At present, there are limited number of researches on the participation of V2G technology in the voltage regulation market. Reference [70] proposed an optimization model for battery SOC in V2G technology participation in the regulation market, which can minimize the battery degradation caused by providing V2G technology services, thereby improving the economic feasibility. Reference [71] proposed a distributed model predictive control strategy for distribution networks, which can utilize V2G technology to aggregate reactive power and integrate it into real-time voltage regulation of the grid.…”

Section: Voltage Regulationmentioning

confidence: 99%

Feasibility and Challenges for Vehicle-to-Grid in Electricity Market: A Review

Wan,

Yu,

Huo

et al. 2024

Energies

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Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology’s business schemes are investigated. Furthermore, the challenges faced by the V2G technology’s business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology’s research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector.

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Section: Voltage Regulationmentioning

confidence: 99%

Feasibility and Challenges for Vehicle-to-Grid in Electricity Market: A Review

Wan,

Yu,

Huo

et al. 2024

Energies

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“…Notable references include for a comprehensive overview of SOC analysis, for an exploration of V2G technology and its potential, and for insights into the economic and regulatory aspects of V2G services. By delving into these sources, this research article provides a holistic perspective on the evolving landscape of EVs in the context of grid integration, SOC analysis, and V2G services, contributing to the advancement of sustainable and responsive energy systems [103][104][105].…”

Section: G Integration Of V2g Services In Soc Analysis For Evsmentioning

confidence: 99%

A Comprehensive Review and Analysis of the Allocation of Electric Vehicle Charging Stations in Distribution Networks

Yuvaraj,

Devabalaji,

Kumar

et al. 2024

IEEE Access

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In recent times, the limited availability of fossil fuels and growing concerns regarding the emission of greenhouse gases (GHGs) have directly impacted the shift from conventional automobiles to electric vehicles (EVs). Additionally, there have been notable advancements in new energy research, which have significantly improved the viability of EVs. Consequently, EVs have gained widespread recognition and have been rapidly adopted in many countries worldwide. However, the rapid growth of EVs has given rise to several challenges, such as insufficient charging infrastructure, unequal distribution, high costs, and a lack of charging stations, which have become increasingly significant. The limited availability of charging facilities is hindering the widespread adoption of EVs. However, as more people embrace EVs, there has been a growth in the installation of electric vehicle charging stations (EVCSs) in public locations. Recent research has focused on identifying the ideal locations for EVCSs in order to assist the electrification of transport systems and meet the growing demand for EVs. A well-developed EVCS infrastructure can help address some key issues facing EVs, such as pricing and range limitations. Researchers have used various methodologies, objective functions, and constraints to formulate the problem of identifying the best sites for EVCSs. Current research is focused on determining the best locations for EVCSs. This endeavor intends to ease the transition to electrified transport networks while also addressing the growing demand for EVs. This review article explores various optimization techniques to achieve optimal solutions while considering the impact of EV charging load on the distribution system (DS), environmental implications, and economic impact. The research used a standard IEEE 33-bus radial distribution system (RDS) with a full variety of potential energy sources to improve understanding of the subject. The use of the bald eagle search algorithm (BESA) and cuckoo search algorithm (CSA) aided in the best identification of energy source locations and their relative capacities. In addition, the examination of EV charging techniques, including both the traditional charging technique (TCT) and the innovative charging technique (ICT), is being undertaken to assess the effectiveness of these approaches. The study included ten separate scenarios, each of which was thoroughly evaluated to demonstrate the individual and synergistic usefulness of various energy sources in mitigating the effects of EV charging on the DS. The collected data from the empirical inquiry was aggregated and thoroughly analyzed.

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“…The OCV is measured by short‐term discharge and long‐term setting. This method shows high accuracy for a single battery 10‐13 . However, the experimental test of OCV costs a lot of time.…”

Section: Introductionmentioning

confidence: 99%

“…This method shows high accuracy for a single battery. [10][11][12][13] However, the experimental test of OCV costs a lot of time. Model-based SOC estimation approaches mainly include electrochemical models (EM), [14][15][16][17][18][19] fractional order models (FOM) [20][21][22][23] and equivalent circuit models (ECM).…”

Section: Introductionmentioning

confidence: 99%

Optimized deep neural network enabled low‐cost state of charge estimation for different kinds of lithium‐ion batteries under dynamic load conditions

Wang

Wei

et al. 2022

Intl J of Energy Research

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Summary To decrease the influence of large dynamic currents on the accuracy of state of charge (SOC) estimation and decrease the cost of model training for different kinds of lithium‐ion batteries, we propose an optimized deep neural network based low‐cost SOC estimation in this paper. First, the data preprocessing method of a moving window is introduced. An optimized moving window is used to preprocess the data to higher dimensions and obtain temporal features. Second, the structure of the proposed approach are thoroughly discussed, and hyperparameters are optimized by the grey wolf optimizer to improve the performance of the deep neural network. Finally, the suggested technique is trained and validated using experimental datasetsacross numerous drive cycles and temperature ranges, as well as compared to other estimators to prove its superiority. The results suggest that the proposed strategy was effective in terms of estimating accuracy and generality. Furthermore, the developed method based transfer learning can quickly adapt to varying battery SOC estimations and achieve good results after fine‐tuning a small number of parameters. The test results revealed that fine‐tuning the parameters of the two layers reduced the root mean square error of different types of batteries to less than 1.75%.

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Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services

Cited by 22 publications

References 27 publications

Feasibility and Challenges for Vehicle-to-Grid in Electricity Market: A Review

Feasibility and Challenges for Vehicle-to-Grid in Electricity Market: A Review

A Comprehensive Review and Analysis of the Allocation of Electric Vehicle Charging Stations in Distribution Networks

Optimized deep neural network enabled low‐cost state of charge estimation for different kinds of lithium‐ion batteries under dynamic load conditions

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