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

By Yi Li, Kailong Liu, Aoife M. Foley, A. A. Zulke, Maitane Berecibar, Elise Nanini-Maury, J. Van Mierlo, Harry Ernst Hoster. Renewable & Sustainable Energy Reviews, 2019

1,002 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/9f8550fdba184ea150566518a50879f1552fc7ea

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Citations in the corpus

Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “Various works in applying techniques such as ANNs, support vector machines (SVM), Gaussian/Bayesian regression, random forest and KF based approaches have been reported for predicting states such as SOC, SOH and RUL which are well summarised by Ng et al. [52] and Li et al. [53] .”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “SOH can be obtained through a complex calculation of the “black box” [16], [17].”