Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Pamuła, T.; Pamuła, W.

doi:10.3390/en13092340

Cited by 70 publications

(25 citation statements)

References 23 publications

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“…The Lagrangian Relaxation algorithm was adopted in [40] to optimize the battery electric bus charging facility location and to fit the BEB fleet size. 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.…”

Section: Literature Studymentioning

confidence: 99%

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

Krawiec

2021

Transport Problems

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Battery electric buses (BEBs) are gaining ground in public transport, and the next decade is likely to witness a further increase in the numbers of these vehicles. The core problem of the BEB deployment process in public transport lies in how to deal with the technical limitations of this technology (primarily, a limited range of a bus). This paper outlines the model of vehicle cycle hierarchization dedicated to municipal transport planners and transit companies. The model is based on a multicriteria decision-making methodology that results in a hierarchy of vehicle cycles, ordered from the most to the least suitable to be operated by electric buses. The proof-of-principle demonstration of the model has been carried out in the Polish medium-sized city of Jaworzno.

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Section: Literature Studymentioning

confidence: 99%

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

Krawiec

2021

Transport Problems

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“…The model used arrival times at bus stops, bus stop locations, and traffic conditions as the input. These parameters were easy to be obtained [29].…”

Section: Literature Reviewsmentioning

confidence: 99%

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

et al. 2021

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Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

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“…For electric buses, in [12], deriving estimations of energy consumption of bus lines was developed using deep learning network. The authors of [13] proposed a microservice-oriented big data architecture incorporating data processing techniques, to achieve smart transportation and analytic microservices.…”

Section: Related Workmentioning

confidence: 99%

“…In most of the published literature, OBD have been used to collect vehicle driving data for analysis of driving behaviors. For example, Nirmali et al [10] and Hwang et al [12] employed OBD to collect vehicle driving data and applied the decision tree classification method and K-means clustering algorithm to analyze the driving behaviors that influenced energy consumption. Their results showed that speed change was a significant influence on energy consumption.…”

Section: Data Preprocessingmentioning

confidence: 99%

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

Lin

Ying

2020

Applied Sciences

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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.

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Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Cited by 70 publications

References 23 publications

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

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

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

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

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