Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-06-15
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.
Keywords
Clustering analysis, Discrete incremental capacity analysis, Electric vehicles, State of health
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