research-article

Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model

Authors:

Stefan Zdravković,

Davor Vujanović,

Dragan PamučarAuthors Info & Claims

Neural Computing and Applications, Volume 33, Issue 18

Pages 11541 - 11554

https://doi.org/10.1007/s00521-021-05823-z

Published: 01 September 2021 Publication History

Abstract

Due to the great market competition, transport companies face the need to reduce their vehicle fleet costs. The vehicle fleet managers’ actions on the driver’s driving style to achieve fuel consumption savings are measures to increase the fleet’s energy efficiency. The authors developed a model for evaluating driving style using a type-2 fuzzy logic system. The model comprehensively considers three parameters: engine speed, accelerator pedal position, and acceleration/deceleration. These parameters can precisely describe the driving style and additionally have a strong influence on fuel consumption. The model output is the driver’s score, representing the influence of driving style to fuel consumption. The model is tested in the company whose drivers have attended the eco-driving training course. Each driver’s driving style was monitored for 15 days to obtain trustworthy assessments regarding driving style. The result was twofold: firstly, we point out the importance of simultaneous observation of all three defined parameters to get reliable driver’s score in terms of driving style, and secondly, it is established that drivers have significantly different driving styles regardless of whether they have attended the same eco-driving training. The established differences in driving styles have a direct impact on the obtained differences in fuel consumption among drivers. The proposed model can significantly reduce fuel consumption depending on the driving style and increase the vehicle fleet’s energy efficiency.

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Cited By

Xu NLi XYue FJia YLiu QZhao D(2023)An Eco-Driving Evaluation Method for Battery Electric Bus Drivers Using Low-Frequency Big DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.326718724:9(9296-9308)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3267187

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cover image Neural Computing and Applications

Neural Computing and Applications Volume 33, Issue 18

Sep 2021

734 pages

ISSN:0941-0643

EISSN:1433-3058

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© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2021

Accepted: 10 February 2021

Received: 31 August 2020

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Ministry of Education, Science and Technological Development (RS)

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Cited By

Xu NLi XYue FJia YLiu QZhao D(2023)An Eco-Driving Evaluation Method for Battery Electric Bus Drivers Using Low-Frequency Big DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.326718724:9(9296-9308)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3267187

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