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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Research output: Contribution to journalJournal article

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Data-driven health estimation and lifetime prediction of lithium-ion batteries : A review. / Li, Y.; Liu, K.; Foley, A.M.; Aragon Zülke, Alana ; Berecibar, M.; Nanini-Maury, E.; Van Mierlo, J.; Hoster, H.E.

In: Renewable and Sustainable Energy Reviews, Vol. 113, 109254, 01.10.2019.

Research output: Contribution to journalJournal article

Harvard

Li, Y, Liu, K, Foley, AM, Aragon Zülke, A, Berecibar, M, Nanini-Maury, E, Van Mierlo, J & Hoster, HE 2019, 'Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review', Renewable and Sustainable Energy Reviews, vol. 113, 109254. https://doi.org/10.1016/j.rser.2019.109254

APA

Li, Y., Liu, K., Foley, A. M., Aragon Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, [109254]. https://doi.org/10.1016/j.rser.2019.109254

Vancouver

Li Y, Liu K, Foley AM, Aragon Zülke A, Berecibar M, Nanini-Maury E et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews. 2019 Oct 1;113. 109254. https://doi.org/10.1016/j.rser.2019.109254

Author

Li, Y. ; Liu, K. ; Foley, A.M. ; Aragon Zülke, Alana ; Berecibar, M. ; Nanini-Maury, E. ; Van Mierlo, J. ; Hoster, H.E. / Data-driven health estimation and lifetime prediction of lithium-ion batteries : A review. In: Renewable and Sustainable Energy Reviews. 2019 ; Vol. 113.

Bibtex

@article{9469addf30e34f2aa3d2e0af717fe936,
title = "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review",
abstract = "Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.",
keywords = "Ageing mechanism, Battery health diagnostics and prognostics, Data-driven approach, Electric vehicle, Lithium-ion battery, Sustainable energy",
author = "Y. Li and K. Liu and A.M. Foley and {Aragon Z{\"u}lke}, Alana and M. Berecibar and E. Nanini-Maury and {Van Mierlo}, J. and H.E. Hoster",
year = "2019",
month = oct
day = "1",
doi = "10.1016/j.rser.2019.109254",
language = "English",
volume = "113",
journal = "Renewable and Sustainable Energy Reviews",
issn = "1364-0321",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Data-driven health estimation and lifetime prediction of lithium-ion batteries

T2 - A review

AU - Li, Y.

AU - Liu, K.

AU - Foley, A.M.

AU - Aragon Zülke, Alana

AU - Berecibar, M.

AU - Nanini-Maury, E.

AU - Van Mierlo, J.

AU - Hoster, H.E.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

AB - Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

KW - Ageing mechanism

KW - Battery health diagnostics and prognostics

KW - Data-driven approach

KW - Electric vehicle

KW - Lithium-ion battery

KW - Sustainable energy

U2 - 10.1016/j.rser.2019.109254

DO - 10.1016/j.rser.2019.109254

M3 - Journal article

VL - 113

JO - Renewable and Sustainable Energy Reviews

JF - Renewable and Sustainable Energy Reviews

SN - 1364-0321

M1 - 109254

ER -