| Title |
Machine Learning Approaches for EV Charging Behavior: A Review |
| ID_Doc |
42629 |
| Authors |
Shahriar, S; Al-Ali, AR; Osman, AH; Dhou, S; Nijim, M |
| Title |
Machine Learning Approaches for EV Charging Behavior: A Review |
| Year |
2020 |
| Published |
|
| Abstract |
As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this article is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed. |
| PDF |
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09194702.pdf
|
