Construction of Analytical Models for Driving Energy Consumption of Electric Buses through Machine Learning

Abstract

In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced energy consumption and operating costs are important operational benefits for passenger transport operators, and driving behavior has a significant impact on fuel consumption. Although many literatures or real-world systems have addressed the issues related to reducing energy consumption with electric buses, these works do not involve the records collected from an on-vehicle battery management system (BMS). Accordingly, the results of analyses of existing works lack in-depth discussions, and therefore the applicability of existing works is insignificant. Therefore, in this study, driving data were collected using a battery management system (BMS), and vehicular power consumption was classified according to energy efficiency. Then, decision trees and random forest were applied to construct energy consumption analytical models. Finally, the driving behaviors that influence energy consumption were investigated. A case study was conducted in which a Taichung passenger transport operator’s electric bus driving data on urban routes were collected to construct energy consumption analytical models. The data consisted of two parts, i.e., vehicle records and route records. On the basis of these records, we considered the practicability and applicability of the analytical models by transforming the unstructured records into raw data. Passenger transport operators and drivers can leverage the obtained eco-driving indicators for different bus routes for energy savings and carbon reduction.

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

“…This point was demonstrated in the research conducted by Pereira et al [65]. Therefore, RF is widely applied in the prediction of fuel consumption [66] and energy consumption [67]. Perrotta et al [10] established three truck fuel consumption prediction models, namely SVM, ANN, and RF, with determination coefficients of 0.83, 0.85, and 0.87, respectively.…”

“…This point was demonstrated in the research conducted by Pereira et al [65]. Therefore, RF is widely applied in the prediction of fuel consumption [66] and energy consumption [67]. Perrotta et al The prediction model of fuel consumption established based on RF can effectively identify the nonlinear relationship between different variables and provide prediction results that are highly correlated with actual fuel consumption, especially the application of the coupling model, which can effectively reduce the error of the fuel consumption prediction model.…”

Energy Consumption of Battery- Electric Buses: Review of Influential Parameters and Modelling Approaches

“…Another data-driven approach for the estimation of EC is machine learning. Lin et al collected 10s data from a bus on one line in Taiwan, for a period of 8 months [53]. They use this data to construct analytical models to forecast the EC of the bus using decision trees and the random forest method.…”

Vehicle Cycle Hierarchization Model to Determine the Order of Battery Electric Bus Deployment in Public Transport

“…In [41], deep learning methods were adopted when estimating BEB energy consumption on real-world data in the Polish municipality of Jaworzno. A genetic algorithm for the energy consumption minimization of BEBs was developed in [42], and a machine learning algorithm is applied in [43] for the same purpose. Wei et.…”