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

被引：61
作者：
Xu, Zhicheng [1 ,2 ]
Wang, Jun [1 ,2 ]
Lund, Peter D. [1 ,3 ]
Zhang, Yaoming [1 ]
机构：
[1] Southeast Univ, Key Lab Solar Energy Sci & Technol Jiangsu Prov, 2 Si Pai Lou, Nanjing 210096, Peoples R China
[2] Southeast Univ, Energy Storage Res Ctr, Sch Energy & Environm, 2 Si Pai Lou, Nanjing 210096, Peoples R China
[3] Aalto Univ, Sch Sci, POB 15100, Espoo 00076, Finland
来源：
ENERGY | 2021年 / 225卷 / 225期
基金：
美国国家科学基金会;
关键词：
Clustering analysis; Electric vehicles; State of health; Discrete incremental capacity analysis; LITHIUM-ION BATTERY; MANAGEMENT-SYSTEM; OF-HEALTH; CHARGE;
D O I：
10.1016/j.energy.2021.120160
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
The accuracy of the state of health (SoH) estimation and prediction is of great importance to the oper-ational 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 pro-posed 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 eval-uate 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. (c) 2021 Elsevier Ltd. All rights reserved.
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页数：14
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