Preparation of Papers for IFAC Conferences & Symposia: A comparative study of remaining useful life predictions for lithium-ion battery

Rui Xiong; Jiahuan Lu

doi:10.1016/j.ifacol.2018.10.048

Preparation of Papers for IFAC Conferences & Symposia: A comparative study of remaining useful life predictions for lithium-ion battery

Rui Xiong, Jiahuan Lu

School of Mechanical Engineering

Beijing Institute of Technology

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

6 Citations (Scopus)

Abstract

Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.

Original language	English
Pages (from-to)	268-273
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	51
Issue number	31
DOIs	https://doi.org/10.1016/j.ifacol.2018.10.048
Publication status	Published - 2018
Event	5th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, E-COSM 2018 - Changchun, China Duration: 20 Sept 2018 → 22 Sept 2018

Keywords

Lithium-ion battery
battery degradation
electric vehicles
remaining useful life

UN SDGs

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

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10.1016/j.ifacol.2018.10.048

Cite this

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title = "Preparation of Papers for IFAC Conferences \& Symposia: A comparative study of remaining useful life predictions for lithium-ion battery",

abstract = "Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.",

keywords = "Lithium-ion battery, battery degradation, electric vehicles, remaining useful life",

author = "Rui Xiong and Jiahuan Lu",

note = "Publisher Copyright: {\textcopyright} 2018; 5th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, E-COSM 2018 ; Conference date: 20-09-2018 Through 22-09-2018",

year = "2018",

doi = "10.1016/j.ifacol.2018.10.048",

language = "English",

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T1 - Preparation of Papers for IFAC Conferences & Symposia

T2 - 5th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, E-COSM 2018

AU - Xiong, Rui

AU - Lu, Jiahuan

PY - 2018

Y1 - 2018

N2 - Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.

AB - Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.

KW - Lithium-ion battery

KW - battery degradation

KW - electric vehicles

KW - remaining useful life

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DO - 10.1016/j.ifacol.2018.10.048

M3 - Conference article

AN - SCOPUS:85056164560

SN - 2405-8963

VL - 51

SP - 268

EP - 273

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 31

Y2 - 20 September 2018 through 22 September 2018

ER -

Preparation of Papers for IFAC Conferences & Symposia: A comparative study of remaining useful life predictions for lithium-ion battery

Abstract

Keywords

UN SDGs

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