An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter

Venu Sangwan; Rajesh Kumar; Akshay Kumar Rathore

doi:10.1109/IICPE.2018.8709470

An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter

Venu Sangwan
, Rajesh Kumar
, Akshay Kumar Rathore

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

In battery power, electric vehicle lifetime and the cost of the battery are the primary concern and challenge in the fast evolving and promising uptake of an electric vehicle. Battery aging leads to gradually deteriorates in battery performance hence identification of battery states and health is essential. Battery health identification helps the customer to maintain and replace batteries in advance to prevent the loss caused by the unexpected failure of these batteries and help in reducing maintenance cost. This paper presents and compares two empirical models to describe battery degradation behaviors over its lifespan. Validation of the accuracy of the empirical model is performed by using experimental life-cycle test data. Subsequently, the most accurate model has been used in an Unscented Kalman Filter to predict battery remaining useful life (RUL). This work provides the initial step towards the development of battery health management system.

Original language	English
Title of host publication	8th IEEE India International Conference on Power Electronics, IICPE 2018
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538649961
DOIs	https://doi.org/10.1109/IICPE.2018.8709470
Publication status	Published - 2 Jul 2018
Externally published	Yes
Event	8th IEEE India International Conference on Power Electronics, IICPE 2018 - Jaipur, India Duration: 13 Dec 2018 → 15 Dec 2018

Publication series

Name	India International Conference on Power Electronics, IICPE
Volume	2018-December
ISSN (Print)	2160-3162
ISSN (Electronic)	2160-3170

Conference

Conference	8th IEEE India International Conference on Power Electronics, IICPE 2018
Country/Territory	India
City	Jaipur
Period	13/12/18 → 15/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Battery Electric Vehicle
Capacity degradation
Health Management
Remaining Useful Life
State of Health
Unscented Kalman Filter

ASJC Scopus subject areas

Energy Engineering and Power Technology
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IICPE.2018.8709470

Cite this

Sangwan, V., Kumar, R., & Rathore, A. K. (2018). An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter. In 8th IEEE India International Conference on Power Electronics, IICPE 2018 Article 8709470 (India International Conference on Power Electronics, IICPE; Vol. 2018-December). IEEE Computer Society. https://doi.org/10.1109/IICPE.2018.8709470

@inproceedings{f5794d748d49421fb0d860be35595632,

title = "An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter",

abstract = "In battery power, electric vehicle lifetime and the cost of the battery are the primary concern and challenge in the fast evolving and promising uptake of an electric vehicle. Battery aging leads to gradually deteriorates in battery performance hence identification of battery states and health is essential. Battery health identification helps the customer to maintain and replace batteries in advance to prevent the loss caused by the unexpected failure of these batteries and help in reducing maintenance cost. This paper presents and compares two empirical models to describe battery degradation behaviors over its lifespan. Validation of the accuracy of the empirical model is performed by using experimental life-cycle test data. Subsequently, the most accurate model has been used in an Unscented Kalman Filter to predict battery remaining useful life (RUL). This work provides the initial step towards the development of battery health management system.",

keywords = "Battery Electric Vehicle, Capacity degradation, Health Management, Remaining Useful Life, State of Health, Unscented Kalman Filter",

author = "Venu Sangwan and Rajesh Kumar and Rathore, \{Akshay Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE India International Conference on Power Electronics, IICPE 2018 ; Conference date: 13-12-2018 Through 15-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/IICPE.2018.8709470",

language = "English",

series = "India International Conference on Power Electronics, IICPE",

publisher = "IEEE Computer Society",

booktitle = "8th IEEE India International Conference on Power Electronics, IICPE 2018",

address = "United States",

}

Sangwan, V, Kumar, R & Rathore, AK 2018, An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter. in 8th IEEE India International Conference on Power Electronics, IICPE 2018., 8709470, India International Conference on Power Electronics, IICPE, vol. 2018-December, IEEE Computer Society, 8th IEEE India International Conference on Power Electronics, IICPE 2018, Jaipur, India, 13/12/18. https://doi.org/10.1109/IICPE.2018.8709470

An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter. / Sangwan, Venu; Kumar, Rajesh; Rathore, Akshay Kumar.
8th IEEE India International Conference on Power Electronics, IICPE 2018. IEEE Computer Society, 2018. 8709470 (India International Conference on Power Electronics, IICPE; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter

AU - Sangwan, Venu

AU - Kumar, Rajesh

AU - Rathore, Akshay Kumar

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In battery power, electric vehicle lifetime and the cost of the battery are the primary concern and challenge in the fast evolving and promising uptake of an electric vehicle. Battery aging leads to gradually deteriorates in battery performance hence identification of battery states and health is essential. Battery health identification helps the customer to maintain and replace batteries in advance to prevent the loss caused by the unexpected failure of these batteries and help in reducing maintenance cost. This paper presents and compares two empirical models to describe battery degradation behaviors over its lifespan. Validation of the accuracy of the empirical model is performed by using experimental life-cycle test data. Subsequently, the most accurate model has been used in an Unscented Kalman Filter to predict battery remaining useful life (RUL). This work provides the initial step towards the development of battery health management system.

AB - In battery power, electric vehicle lifetime and the cost of the battery are the primary concern and challenge in the fast evolving and promising uptake of an electric vehicle. Battery aging leads to gradually deteriorates in battery performance hence identification of battery states and health is essential. Battery health identification helps the customer to maintain and replace batteries in advance to prevent the loss caused by the unexpected failure of these batteries and help in reducing maintenance cost. This paper presents and compares two empirical models to describe battery degradation behaviors over its lifespan. Validation of the accuracy of the empirical model is performed by using experimental life-cycle test data. Subsequently, the most accurate model has been used in an Unscented Kalman Filter to predict battery remaining useful life (RUL). This work provides the initial step towards the development of battery health management system.

KW - Battery Electric Vehicle

KW - Capacity degradation

KW - Health Management

KW - Remaining Useful Life

KW - State of Health

KW - Unscented Kalman Filter

UR - https://www.scopus.com/pages/publications/85065875753

U2 - 10.1109/IICPE.2018.8709470

DO - 10.1109/IICPE.2018.8709470

M3 - Conference contribution

AN - SCOPUS:85065875753

T3 - India International Conference on Power Electronics, IICPE

BT - 8th IEEE India International Conference on Power Electronics, IICPE 2018

PB - IEEE Computer Society

T2 - 8th IEEE India International Conference on Power Electronics, IICPE 2018

Y2 - 13 December 2018 through 15 December 2018

ER -

Sangwan V, Kumar R, Rathore AK. An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter. In 8th IEEE India International Conference on Power Electronics, IICPE 2018. IEEE Computer Society. 2018. 8709470. (India International Conference on Power Electronics, IICPE). doi: 10.1109/IICPE.2018.8709470

An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this