Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries

Xiaopeng TANG; Yujie WANG; Qi LIU; Furong GAO

doi:10.1016/j.isci.2021.103103

Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries

Xiaopeng TANG, Yujie WANG^*, Qi LIU, Furong GAO^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

38 Citations (Scopus)

Abstract

The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.

Original language	English
Article number	103103
Journal	iScience
Volume	24
Issue number	10
Early online date	10 Sept 2021
DOIs	https://doi.org/10.1016/j.isci.2021.103103
Publication status	Published - 22 Oct 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

Funding

The first author would like to thank the Guangzhou HKUST Fok Ying Tung Research Institute for the continuing support during the Hong Kong's unrest and 2019-nCoV's outbreak. This work is supported partly by the National Natural Science Foundation of China (Grant No. 61803359 ), the University Synergy Innovation Program of Anhui Province (Grant No. GXXT-2019-002 ), Guangdong Scientific and Technological Project (Grant No. 2017B010120002 ), Guangzhou Scientific and Technological Project (Grant No. 202002030323 ), Hong Kong Research Grant Council (Grant No. 16207717 and 16208520 ), and the Shenzhen Science and Technology Innovation Commission under the grant Shenzhen-Hong Kong Innovation Circle Category D Project: SGDX 2019081623240948 .

Keywords

Energy Management
Energy storage
Energy Systems

UN SDGs

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

Access to Document

10.1016/j.isci.2021.103103Licence: CC BY-NC-ND

Cite this

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title = "Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries",

abstract = "The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.",

keywords = "Energy Management, Energy storage, Energy Systems",

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AU - TANG, Xiaopeng

AU - WANG, Yujie

AU - LIU, Qi

AU - GAO, Furong

PY - 2021/10/22

Y1 - 2021/10/22

N2 - The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.

AB - The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.

KW - Energy Management

KW - Energy storage

KW - Energy Systems

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Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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