Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles

Honglei Dong; Jie Fan; Yuan Zou; Lingyun Xiao; Bo Deng; Yang Ou; Yong Yang; Jing Guan

doi:10.1088/1755-1315/585/1/012141

Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles

Honglei Dong
, Jie Fan^*
, Yuan Zou
, Lingyun Xiao
, Bo Deng
, Yang Ou
, Yong Yang
, Jing Guan

^*Corresponding author for this work

School of Mechanical Engineering

SAMR Defective Product Administrative Center
China Automotive Engineering Research Institute Corporation

Research output: Contribution to journal › Conference article › peer-review

7 Citations (Scopus)

Abstract

Robust and accurate state-of-health (SOH) estimation is significant for safe and reliable operation of electric vehicles (EVs). In this paper, the robustness analysis of a specific IC analysis method based on probability density function is investigated in depth. Four aging features frequently used by existing researches to estimate SOH are extracted from IC curve. The influence of temperature and current rate on these four aging features as battery ages is analyzed in detail. These aging features' sensitiveness to data length and their appearance frequency in real-world charging events are also included in deciding which one is best for SOH estimation. Finally, one aging feature that has greatest potential to be used on EVs for SOH estimation is selected out.

Original language	English
Article number	012141
Journal	IOP Conference Series: Earth and Environmental Science
Volume	585
Issue number	1
DOIs	https://doi.org/10.1088/1755-1315/585/1/012141
Publication status	Published - 3 Nov 2020
Event	2020 6th International Conference on Energy, Environment and Materials Science, EEMS 2020 - Hulun Buir, China Duration: 28 Aug 2020 → 30 Aug 2020

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Cite this

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title = "Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles",

abstract = "Robust and accurate state-of-health (SOH) estimation is significant for safe and reliable operation of electric vehicles (EVs). In this paper, the robustness analysis of a specific IC analysis method based on probability density function is investigated in depth. Four aging features frequently used by existing researches to estimate SOH are extracted from IC curve. The influence of temperature and current rate on these four aging features as battery ages is analyzed in detail. These aging features' sensitiveness to data length and their appearance frequency in real-world charging events are also included in deciding which one is best for SOH estimation. Finally, one aging feature that has greatest potential to be used on EVs for SOH estimation is selected out.",

author = "Honglei Dong and Jie Fan and Yuan Zou and Lingyun Xiao and Bo Deng and Yang Ou and Yong Yang and Jing Guan",

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 2020 6th International Conference on Energy, Environment and Materials Science, EEMS 2020 ; Conference date: 28-08-2020 Through 30-08-2020",

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T1 - Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles

AU - Dong, Honglei

AU - Fan, Jie

AU - Zou, Yuan

AU - Xiao, Lingyun

AU - Deng, Bo

AU - Ou, Yang

AU - Yang, Yong

AU - Guan, Jing

PY - 2020/11/3

Y1 - 2020/11/3

N2 - Robust and accurate state-of-health (SOH) estimation is significant for safe and reliable operation of electric vehicles (EVs). In this paper, the robustness analysis of a specific IC analysis method based on probability density function is investigated in depth. Four aging features frequently used by existing researches to estimate SOH are extracted from IC curve. The influence of temperature and current rate on these four aging features as battery ages is analyzed in detail. These aging features' sensitiveness to data length and their appearance frequency in real-world charging events are also included in deciding which one is best for SOH estimation. Finally, one aging feature that has greatest potential to be used on EVs for SOH estimation is selected out.

AB - Robust and accurate state-of-health (SOH) estimation is significant for safe and reliable operation of electric vehicles (EVs). In this paper, the robustness analysis of a specific IC analysis method based on probability density function is investigated in depth. Four aging features frequently used by existing researches to estimate SOH are extracted from IC curve. The influence of temperature and current rate on these four aging features as battery ages is analyzed in detail. These aging features' sensitiveness to data length and their appearance frequency in real-world charging events are also included in deciding which one is best for SOH estimation. Finally, one aging feature that has greatest potential to be used on EVs for SOH estimation is selected out.

UR - https://www.scopus.com/pages/publications/85096496619

U2 - 10.1088/1755-1315/585/1/012141

DO - 10.1088/1755-1315/585/1/012141

M3 - Conference article

AN - SCOPUS:85096496619

SN - 1755-1307

VL - 585

JO - IOP Conference Series: Earth and Environmental Science

JF - IOP Conference Series: Earth and Environmental Science

IS - 1

M1 - 012141

T2 - 2020 6th International Conference on Energy, Environment and Materials Science, EEMS 2020

Y2 - 28 August 2020 through 30 August 2020

ER -

Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles

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