Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

Hani Mustafataher M Almadni; Babakalli Alkali; Gyorgy Balint Lak; Ray Ansell

Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

Hani Mustafataher M Almadni, Babakalli Alkali, Gyorgy Balint Lak, Ray Ansell

Department of Mechanical Engineering

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

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Abstract

The use of lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charge and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and, in this paper, the degradation mechanisms associated with changes in the electrical impedance, capacity and the lifecycle of the battery cells are explored. 18650 battery cells are tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures, ranging from 20°C to 55°C at a constant time per experiment. Statistical analysis of the data is conducted using a time series approach and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance.

Original language	English
Title of host publication	Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019
Number of pages	13
Publication status	Published - 27 Jun 2019

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Almadni, H.M.M. et al (2019) Time series analysis of high energy density lithium-ion batteries for electric vehicles applications
Copyright © 2019 The British Institute of Non-Destructive Testing. This is the final published version of the work: Almadni, HMM, Alkali, B, Lak, GB & Ansell, R 2019, 'Time series analysis of high energy density lithium-ion batteries for electric vehicles applications'. in Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019. The event website is available at: https://www.bindt.org/events/CM2019/
Final published version, 1.9 MB

https://www.bindt.org/events/CM2019/305-abstract/

Cite this

@inproceedings{41d8c786d6214046b331e143f8cb74ce,

title = "Time series analysis of high energy density lithium-ion batteries for electric vehicles applications",

abstract = "The use of lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charge and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and, in this paper, the degradation mechanisms associated with changes in the electrical impedance, capacity and the lifecycle of the battery cells are explored. 18650 battery cells are tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures, ranging from 20°C to 55°C at a constant time per experiment. Statistical analysis of the data is conducted using a time series approach and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance.",

author = "Almadni, {Hani Mustafataher M} and Babakalli Alkali and Lak, {Gyorgy Balint} and Ray Ansell",

note = "Event: 25-27 June 2019, Principal Grand Central Hotel, Glasgow, UK. CM-MFPT-0048-2019. R1 Pub date: used last date of conference. Acceptance in SAN Publisher policy unknown - made file open and contacted event contact. ET 16/7/19 ",

year = "2019",

month = jun,

day = "27",

language = "English",

booktitle = "Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019",

}

Time series analysis of high energy density lithium-ion batteries for electric vehicles applications. / Almadni, Hani Mustafataher M; Alkali, Babakalli; Lak, Gyorgy Balint et al.

Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019. 2019.

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

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T1 - Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

AU - Almadni, Hani Mustafataher M

AU - Alkali, Babakalli

AU - Lak, Gyorgy Balint

AU - Ansell, Ray

N1 - Event: 25-27 June 2019, Principal Grand Central Hotel, Glasgow, UK. CM-MFPT-0048-2019. R1 Pub date: used last date of conference. Acceptance in SAN Publisher policy unknown - made file open and contacted event contact. ET 16/7/19

PY - 2019/6/27

Y1 - 2019/6/27

N2 - The use of lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charge and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and, in this paper, the degradation mechanisms associated with changes in the electrical impedance, capacity and the lifecycle of the battery cells are explored. 18650 battery cells are tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures, ranging from 20°C to 55°C at a constant time per experiment. Statistical analysis of the data is conducted using a time series approach and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance.

AB - The use of lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charge and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and, in this paper, the degradation mechanisms associated with changes in the electrical impedance, capacity and the lifecycle of the battery cells are explored. 18650 battery cells are tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures, ranging from 20°C to 55°C at a constant time per experiment. Statistical analysis of the data is conducted using a time series approach and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance.

M3 - Conference contribution

BT - Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019

ER -

Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

Abstract

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