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Dear Colleagues,

This issue is devoted to the highly qualified selected papers of the 11th European Conference on Renewable Energy Systems (ECRES2023, ecres.net), which will take place in Riga, Latvia, in a hybrid format. This international event is expected to involve participants from more than 60 countries.

ECRES aims to bring together researchers, engineers and natural scientists from all over the world who are interested in the advancement of all branches of renewable energy systems. Wind, solar, hydrogen, hydro-, geothermal, solar-concentrating, fuel cell, energy harvesting and other energy-related topics are welcome.

We are open to the inclusion of the following related topics:

Hydrogen energy system design and implementation;
Policies on renewable energy;
Energy system optimization;
Smart systems;
Energy statistics and efficiency;
Electric networks;
Thermodynamical issues on energy;
Wind energy systems;
Turbine designs and implementation;
Photovoltaic material characterization;
Electrical machine design and implementation;
Plasma systems for nuclear fusion.

The papers for this Special Issue have already been submitted to the ECRES 2023 conference via the link https://cmt3.research.microsoft.com/ECRES2023 and will be presented in the conference. After approval, successful papers will be directed to the journal.

Prof. Dr. Erol Kurt
Dr. Jose Manuel Lopez-Guede
Guest Editors

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Keywords

hydrogen
optimization
thermodynamics
wind
photovoltaics
electrical machine

Published Papers (1 paper)

Research

Open AccessArticle

Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

Witsarut Achariyaviriya

and

Energies 2023, 16(17), 6351; https://doi.org/10.3390/en16176351 - 01 Sep 2023

Cited by 1 | Viewed by 364

Abstract

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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