Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time

ABSTRACT

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models.

KEYWORDS:

• Electric vehicles (EVs)
• charging time
• machine learning (ML) algorithm
• gray wolf optimizer (GWO)
• Real-world data

Acknowledgments

The authors thanks the Japan Automobile Research Institute (JARI) for providing the dataset used in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51378091, 71871043].