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

Krawiec, Krzysztof

doi:10.21307/tp-2021-009

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…The development of electromobility in smart cities in Poland is an important research topic [26]. The case study was performed for data obtained from a public transport company (PKM Jaworzno) in the city of Jaworzno in Poland, which has been successfully introducing battery electric buses into its fleet for several years [27].…”

Section: Energy Consumption Models and Neural Networkmentioning

confidence: 99%

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Pamuła

Pamula²

2022

Energies

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The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.

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Section: Energy Consumption Models and Neural Networkmentioning

confidence: 99%

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Pamuła

Pamula²

2022

Energies

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Development of the algorithm for selecting a rational regular urban route for electric bus operation

Горбунова

Smirnova

2021

Vestn. SibADI

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Introduction. The article presents an analysis of researches on the operation of public transport vehicles with an electric drive. Electric buses have been tested in megacities, and despite the advantages, problems were identi- fied both in the selection of the charging type, technological features and with the availability and operation of the charging infrastructure. The operation process is associated with a number of technical restrictions, which in prac- tice make it difficult to select routes for the electric bus operation. The aim of the study is to develop an algorithm for selecting a rational regular urban route for the electric bus operation and to test it on the example of the route network of Tyumen.Materials and methods. A structural diagram of the factors influencing the selection of the route for the electric bus operation has been formed. As a result of processing expert assessments, the most significant factors were identified. The diagram of the interconnections of these factors is identified and limitations for the algorithm are for- mulated based on the theory of systems analysis. The algorithm for selecting the rational regular urban route for the electric bus operation has been developed. It enables creating the simulation model for selecting a rational regular urban route for the operation of an electric bus.Results. The diagram of factors influencing the selection of a rational regular urban route for the operation of an electric bus, taking into account their mutual relations, has been formed. The algorithm and simulation model for se- lecting a rational regular urban route for the operation of an electric bus has been developed. Several urban routes in Tyumen were assessed for the rationality of the introduction of electric buses.Discussion and conclusions. Evaluation of five regular urban routes in Tyumen using the developed algorithm and the simulation model revealed three rational routes, and also showed the inefficiency of the other two. It due to the discrepancy between the duration of the charging session and breaks in operation, as well as the insufficient charge level of the traction battery to ensure the required autonomous movement of the vehicle. In the course of the continuation of these studies, the developed algorithm will be supplemented with the stage of economic assess- ment of routes for the operation of electric buses.

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

Cited by 2 publications

References 27 publications

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Development of the algorithm for selecting a rational regular urban route for electric bus operation

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