Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction

Growing electric mobility makes it difficult for electric vehicles (EVs) to charge adequately while charging infrastructure capacities are limited. Due to the prolonged charging times, precise planning is needed, which necessitates knowing the availability of charging stations. In addition, inconsistencies in charging facilities and illogical charging arrangements cause partial queuing and idling of charging stations. To tackle these issues, it is necessary to first understand EV charging station choice behavior and its influence. This study examines EV charging station choice behavior and aims to find the best prediction method. This study implements a novel interpretable machine learning (ML) framework to predict EVs' charging station choice behavior. The experiment was based on two years of real-world normal and fast charging event data from 500 EVs in Japan. The results revealed that the XGBoost model achieved the highest accuracy compared to the other ML classifiers in predicting charging station choice behavior. Furthermore, this study employed the newly developed SHAP approach to identify feature importance and the complex nonlinear and interactive effects of various attributes on charging station choice behavior. This study suggests that combining ML models with SHAP has the potential to develop an interpretable ML model for predicting EV charging station choice behavior.

Automated summary

Growing electric mobility poses challenges to the charging adequacy of electric vehicles (EVs) due to limited charging infrastructure capacities. This study utilizes an interpretable machine learning framework to predict EVs' charging station choice behavior, with the XGBoost model achieving the highest accuracy in prediction. It also employs the newly developed SHAP approach to identify feature importance and the effects of different attributes on charging station choice behavior.