aalto1 untyped-item.component.html
Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
Series
Energy, Volume 225
Abstract
The accuracy of the state of health (SoH) estimation and prediction is of great importance to the operational effectiveness and safety of electric vehicles. Present approaches mostly employ data-driven analysis with laboratory measurements to determine these parameters. Here a novel method is proposed using discrete incremental capacity analysis based on real-life driving data, which enables to estimate the battery SoH without any prior detailed knowledge of battery internal specifics such as current capacity/resistance information. The method accounts for the battery characteristics. It is robust, highly compatible, and has a short computing time and low memory requirement. It's capable to evaluate the SoH of various type of electric vehicles under different charging strategies. The short computing time and low memory needed for the SoH estimation also demonstrates its potential for practical use. Moreover, the clustering analysis is presented, which provides SoH comparison information of certain EV to that of EVs belonging to same type.
Description
Funding Information: This work is based on a first prize competition entry by one of the authors (Xu) to the National College New Energy Vehicle Big Data Application Innovation Competition ( http://www.ncbdc.top/ ) organized by the National Big Data Alliance of New Energy Vehicles (NDANEV: http://www.ndanev.com/ ). The authors thank NDANEV for providing data on the EVs. The financial support by the National Science Foundation of China (Grant number 51736006 ) is greatly acknowledged. This work was partially supported by Aalto University . Publisher Copyright: © 2021 Elsevier Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Other note
Citation
Xu, Z, Wang, J, Lund, P D & Zhang, Y 2021, 'Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data', Energy, vol. 225, 120160. https://doi.org/10.1016/j.energy.2021.120160