Cycle-life curves determination and modelling of commercially available electric vehicle batteries

被引：0

作者：

Saldaña G. ^{[1
]}

San Martín J.I. ^{[2
]}

Asensio F.J. ^{[2
]}

Zamora I. ^{[3
]}

Oñederra O. ^{[3
]}

González-Pérez M. ^{[2
]}

Oleagordía I.J. ^{[4
]}

机构：

[1] Department of Systems and Automatic Engineering Engineering School of Bilbao, University of the Basque Country, Pza. Ingeniero Torres Quevedo, 1, Bilbao

[2] Department of Electrical Engineering Engineering School of Gipuzkoa, University of the Basque Country, Avda. Otaola, 29, Eibar

[3] Department of Electrical Engineering Engineering School of Bilbao, University of the Basque Country, Pza. Ingeniero Torres Quevedo, 1, Bilbao

[4] Department of Electronic Technology Engineering School of Bilbao, University of the Basque Country, Pza. Ingeniero Torres Quevedo, 1, Bilbao

来源：

Renewable Energy and Power Quality Journal | 2021年 / 19卷

关键词：

Battery; Degradation; Electric vehicle; Li-Ion-NMC; Model;

D O I：

10.24084/repqj19.278

中图分类号：

学科分类号：

摘要：

In recent decades, there has been a growing concern about the trend of global emissions, and in particular those of the transport sector. In this context, the electric vehicle is a promising technology, with some barriers still to be overcome. Among these deficiencies everything related to storage technology is found. In this sense, lithium-ion batteries are one of the options to be considered, although it is necessary to continuously monitor the state of health. Cycle life vs DoD curves are very useful for characterizing profitability in any application that considers battery storage, as well as life cycle optimization studies. Cycle life refers to the number of charge-discharge cycles that a battery can provide before performance decreases to an extent that it cannot perform the required functions (e.g., 80% compared to a fresh one in electromobility applications). In this paper, a model for calculating the Cycle Life vs DoD curves is proposed, applied to a commercially available electric vehicle, the Renault Zoe. Modelling results show R squared coefficient of determinations above 0.9890. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

引用

页码：287 / 292

页数：5

共 15 条

[1] Analysis: Global CO2 Emissions Set to Rise 2% in 2017 after Three-Year ‘Plateau’, (2018)
[2] Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations, (2017)
[3] World Energy Statistics 2017, (2017)
[4] Zamora I., San Martin J.I., Garcia J., Asensio F.J., Onederra O., San Martin J.J., Aperribay V., PEM fuel cells in applications of urban public transport, Renew. Energy Power Qual. J, 1, pp. 599-604, (2011)
[5] Yan J., Li C., Xu G., Xu Y., A novel on-line self-learning state-of-charge estimation of battery management system for hybrid electric vehicle, Proceedings of the 2009 IEEE Intelligent Vehicles Symposium (IVS), pp. 1161-1166
[6] Shen J., Dusmez S., Khaligh A., An advanced electro-thermal cycle-lifetime estimation model for LiFePO4 batteries, Proceedings of the 2013 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1-6
[7] Eddahech A., Briat O., Vinassa J., Real-Time SOC and SOH Estimation for EV Li-Ion Cell Using Online Parameters Identification, Proceedings of the 2012 IEEE Energy Conversion Congress and Exposition, (2012)
[8] Hentunen A., Lehmuspelto T., Suomela J., Electrical battery model for dynamic simulations of hybrid electric vehicles, Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1-6
[9] Lombardi L., Tribioli L., Cozzolino R., Bella G., Comparative environmental assessment of conventional, electric, hybrid, and fuel cell powertrains based on LCA, Int. J. Life Cycle Assess, 22, pp. 1989-2006, (2017)
[10] Thomas C.E., Fuel cell and battery electric vehicles compared, Int. J. Hydrogy Energy, 34, pp. 6005-6020, (2009)

← 1 2 →