Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Abstract

Summary Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time.

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand

“…A variety of ensemble approaches were used in a recent study on demand prediction to anticipate the charging time for EVs [ 26 ]. Eddine et al [ 27 ] suggested a new deep learning method for charging demand predicting for EVs that achieved good prediction results by experimenting with two datasets.…”

Prediction of return on equity of the energy industry based on equity characteristics

“…XGBoost, AdaBoost, and Bagging were the employed soft computing techniques (Shen et al, 2022). The research by Ullah et al (2022) used four different ensemble machine learning (EML) algorithms: random forest, XGBoost, categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. Mao et al (2022) developed a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains.…”

Optimal Deployment of Electric Vehicles’ Fast-Charging Stations

“…Green transportation is essential to address climate change mitigation since they minimize CO 2 and other pollutants that are frequently used in conventional vehicles [12,13]. Amongst all green transportation choices, e-bikes, shared mobility, electric vehicles (EVs), and bus rapid transit are an intriguing option for addressing the aforementioned challenges [14,15].…”

“…Charging infrastructures are becoming crucial components for adopting EVs, connected to vehicle technology and efciency and the accessibility of a reliable power supply to charging stations [12]. It is also tied with the increased electricity demand in other sectors [19].…”