Evaluating battery electric vehicle usage in the EU: A comparative study based on member state energy mixes

被引:2
|
作者
Kocsis, Denes [1 ]
Kiss, Judit T. [2 ]
Arpad, Istvan W. [3 ]
机构
[1] Univ Debrecen, Fac Engn, Dept Environm Engn, H-4032 Debrecen, Hungary
[2] Univ Debrecen, Fac Engn, Dept Engn Management & Enterprise, H-4032 Debrecen, Hungary
[3] Univ Debrecen, Fac Engn, Dept Mech Engn, H-4032 Debrecen, Hungary
关键词
Carbon emission; Energy efficiency; Electric vehicle; Energy conversion;
D O I
10.1016/j.heliyon.2024.e30655
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The transport sector is undergoing a major transformation, as battery electric vehicles (BEV) are gaining ground. Therefore, assessing the sustainability aspects of their use is crucial to obtaining a clear picture of the sector. This article aims to meet this requirement by using European Union (EU) data for the period 2011 to 2021 and focuses not only on EU-27 aggregates but also on each member state separately. For the evaluation, a well -to -wheel (WTW) method was used to calculate two parameters: energy -specific CO 2 emissions ( epsilon) and total efficiency of energy conversions, transmission, and battery ( eta total ). For these values, the annual electricity mixes of the countries were tracked in 5 + 1 categories (combined cycle gas turbine (CCGT), thermal power plant, biofuels, nuclear power plant (NPP), renewables, and imports). The calculated results were illustrated by sustainability matrices describing the former and current positions of the countries. The EU-27 aggregate achieved a 0.04 increase (from 0.37 to 0.41) in total efficiency and a 29 gCO 2 /MJ motion decrease (from 113 to 84 gCO 2 /MJ motion ) during the period. This epsilon value for 2021 was around half the world average. However, very significant differences were identified between member states, which are also assessed in the article with special emphases on the five most populated EU countries (Germany, France, Italy, Spain, and Poland).
引用
收藏
页数:11
相关论文
共 50 条
  • [31] State of Charge Estimation Based on Microscopic Driving Parameters for Electric Vehicle's Battery
    Yao, Enjian
    Wang, Meiying
    Song, Yuanyuan
    Yang, Yang
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2013, 2013
  • [32] Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data
    Li, Xiaoyu
    Wang, Tengyuan
    Wu, Chuxin
    Tian, Jindong
    Tian, Yong
    WORLD ELECTRIC VEHICLE JOURNAL, 2021, 12 (04):
  • [33] Battery State-of-charge Estimation based on H∞ Filter for Hybrid Electric Vehicle
    Yan, Jingyu
    Xu, Guoqing
    Xu, Yangsheng
    Xie, Benliang
    2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4, 2008, : 464 - +
  • [34] A comparative study of AC and DC public electric vehicle charging station usage in Western Australia
    Lim, Kai Li
    Speidel, Stuart
    Braunl, Thomas
    RENEWABLE AND SUSTAINABLE ENERGY TRANSITION, 2022, 2
  • [35] Battery matching of load isolated electric vehicle based on operating mode energy consumption
    Song, Cuiping
    Zhang, Hongxin
    Yin, Huaixian
    Zhang, Tiezhu
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON ADVANCED DESIGN AND MANUFACTURING ENGINEERING, 2015, 39 : 365 - 370
  • [36] Study on Mechanical Properties with Effect of Super Capacitor in Energy Feedback of Electric Vehicle Battery
    Lou, HaiXing
    Yao, Wei
    ADVANCED RESEARCH ON MECHANICAL ENGINEERING, INDUSTRY AND MANUFACTURING ENGINEERING III, 2013, 345 : 22 - 26
  • [37] Modeling energy consumption for battery electric vehicles based on in-use vehicle trajectories
    Zhai, Zhiqiang
    Zhang, Leqi
    Song, Guohua
    Li, Xiao
    Yu, Lei
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2024, 137
  • [38] An MPC-Based Control Strategy for Electric Vehicle Battery Cooling Considering Energy Saving and Battery Lifespan
    Xie, Yi
    Wang, Chenyang
    Hu, Xiaosong
    Lin, Xianke
    Zhang, Yangjun
    Li, Wei
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (12) : 14657 - 14673
  • [39] A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle
    Wang, Hanchen
    Ye, Yiming
    Zhang, Jiangfeng
    Xu, Bin
    ENERGY, 2023, 266
  • [40] Simulation based study of battery electric vehicle performance in real world cycles
    Dhand, Aditya
    Pullen, Keith
    INTERNATIONAL JOURNAL OF ELECTRIC AND HYBRID VEHICLES, 2013, 5 (04) : 327 - 343