Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features

Abstract

Accurate prediction of electric bus energy consumption is a key step to realize the orderly planned charging of electric buses. Meanwhile, to address the problem that the current electric bus energy consumption prediction model is not conducive to realistic application, this paper proposes an energy consumption prediction model that considers actual electric bus operation data to predict trip energy consumption. First, based on the operation data of six routes in Beijing, the influencing factors of electric bus energy consumption are summarized, including route name, travel direction, weekday and nonweekday, operation time, vehicle number, and driver’s name. Secondly, the energy consumption influencing factors were used to extract trip energy consumption features, including departure moment features, vehicle performance features, and driver attribute features. A new simple method is proposed to deal with un-ordered characteristic data to solve the problem of quantifying the influencing factors. The energy consumption prediction model considering actual quantifiable features utilizes the concept of distance to identify several historical trips that have characteristics most similar to the predicted trip in terms of energy consumption. The new prediction model is essentially a machine learning model based on k-means clustering algorithm, which leverages feature extraction and data analysis to make predictions. Finally, the real data are used to predict the energy consumption of different routes and different driving directions on weekdays, respectively. The energy consumption prediction error is as low as 7.112%, and the prediction results are compared with other traditional prediction models, and the model accuracy is high.