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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Artificial Intelligence techniques in Vehicle-to-Grid (V2G) systems: A review, comparative study, and model evaluation
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering

Project
<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)