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

Yi Li, Kailong Liu*, Aoife M. Foley, Alana Zülke, Maitane Berecibar, Elise Nanini-Maury, Joeri Van Mierlo, Harry E. Hoster

*Corresponding author for this work

Research output: Contribution to journalReview article

46 Citations (Scopus)
117 Downloads (Pure)

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.

Original languageEnglish
Article number109254
Number of pages18
JournalRenewable and Sustainable Energy Reviews
Volume113
Early online date12 Jul 2019
DOIs
Publication statusPublished - Oct 2019

Keywords

  • Ageing mechanism
  • Battery health diagnostics and prognostics
  • Data-driven approach
  • Electric vehicle
  • Lithium-ion battery
  • Sustainable energy

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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