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

Kuan-Cheng Lin,Josh Jia-Ching Ying,Chuan-Neng Lin

doi:10.3390/app10176088

Kuan-Cheng Lin, Josh Jia-Ching Ying + Show 1 more

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https://doi.org/10.3390/app10176088

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Journal: Applied sciences	Publication Date: Sep 2, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: National Chung Hsing University

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.

Highlights

IntroductionExcessive use of fossil fuels results in the emission of large quantities of greenhouse gases such as carbon dioxide
Global warming is a climate issue of concern for much of the world
The government has promoted the use of low-pollution, energy-efficient, and intelligent transportation tools, such as the use of electric buses instead of diesel buses, to achieve energy savings and carbon reduction

Summary

Introduction

Excessive use of fossil fuels results in the emission of large quantities of greenhouse gases such as carbon dioxide. The emission of greenhouse gases into the atmosphere leads to higher global temperatures. Governments and global organizations have formulated energy saving and carbon reduction policies. The transport sector is one of the main sources of carbon emissions in most countries, among which the carbon emissions of passenger cars for road transport account for the highest proportion [1]. To reduce the energy consumed and carbon emitted by the transportation sector, the Taiwan government has formulated green transportation policies and advocated traveling on public transport instead of using passenger cars. The government has promoted the use of low-pollution, energy-efficient, and intelligent transportation tools, such as the use of electric buses instead of diesel buses, to achieve energy savings and carbon reduction

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