Artificial Intelligence techniques in Vehicle-to-Grid (V2G) systems: A review, comparative study, and model evaluation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258583" target="_blank" >RIV/61989100:27240/25:10258583 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2352152X25028683" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352152X25028683</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.est.2025.118155" target="_blank" >10.1016/j.est.2025.118155</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Intelligence techniques in Vehicle-to-Grid (V2G) systems: A review, comparative study, and model evaluation

Popis výsledku v původním jazyce

The increasing adoption of electric vehicles (EVs) has positioned Vehicle-to-Grid (V2G) systems as a cornerstone for the integration of renewable energy into the power grid. By enabling bidirectional energy flow, V2G systems contribute to grid stability, load balancing, and peak shaving. However, the complexity of real-time energy management in V2G requires advanced solutions. Artificial Intelligence (AI) techniques have emerged as powerful tools for optimizing various aspects of V2G, including demand prediction, scheduling, battery health monitoring, and grid stabilization. This paper reviews state-of-the-art AI methods applied to V2G systems, categorizing them into machine learning, deep learning, and optimization algorithms. A comprehensive literature review highlights the development trajectory, challenges, and achievements in applying AI to V2G systems. A comparative study evaluates these models based on accuracy, efficiency, scalability, and adaptability. Additionally, a case study on implementing an LSTM-ILP hybrid model for V2G optimization in a residential community demonstrates practical application and performance benefits. Insights into future research directions are also provided.

Název v anglickém jazyce

Artificial Intelligence techniques in Vehicle-to-Grid (V2G) systems: A review, comparative study, and model evaluation

Popis výsledku anglicky

The increasing adoption of electric vehicles (EVs) has positioned Vehicle-to-Grid (V2G) systems as a cornerstone for the integration of renewable energy into the power grid. By enabling bidirectional energy flow, V2G systems contribute to grid stability, load balancing, and peak shaving. However, the complexity of real-time energy management in V2G requires advanced solutions. Artificial Intelligence (AI) techniques have emerged as powerful tools for optimizing various aspects of V2G, including demand prediction, scheduling, battery health monitoring, and grid stabilization. This paper reviews state-of-the-art AI methods applied to V2G systems, categorizing them into machine learning, deep learning, and optimization algorithms. A comprehensive literature review highlights the development trajectory, challenges, and achievements in applying AI to V2G systems. A comparative study evaluates these models based on accuracy, efficiency, scalability, and adaptability. Additionally, a case study on implementing an LSTM-ILP hybrid model for V2G optimization in a residential community demonstrates practical application and performance benefits. Insights into future research directions are also provided.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Energy Storage

ISSN

2352-152X

e-ISSN

2352-1538

Svazek periodika

135

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

nestránkováno

Kód UT WoS článku

001569002300007

EID výsledku v databázi Scopus

2-s2.0-105014930085