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

Li, Y. and Liu, K. and Foley, A.M. and Aragon Zülke, Alana and Berecibar, M. and Nanini-Maury, E. and Van Mierlo, J. and Hoster, H.E. (2019) Data-driven health estimation and lifetime prediction of lithium-ion batteries:A review. Renewable and Sustainable Energy Reviews, 113. ISSN 1364-0321

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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.

Item Type:
Journal Article
Journal or Publication Title:
Renewable and Sustainable Energy Reviews
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2100/2105
Subjects:
ID Code:
135772
Deposited By:
Deposited On:
28 Jan 2020 16:25
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Sep 2020 04:48