Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. (October 2019)

• Record Type: Journal Article
• Authors: Li, Yi; Liu, Kailong; Foley, Aoife M.; Zülke, Alana; Berecibar, Maitane; Nanini-Maury, Elise; Van Mierlo, Joeri; Hoster, Harry E.
• Is Part Of: Renewable & sustainable energy reviews. Volume 113(2019)
• Publication Date: 2019-10
• Subjects: Lithium-ion battery -- Data-driven approach -- Ageing mechanism -- Battery health diagnostics and prognostics -- Electric vehicle -- Sustainable energy; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.109254
• Languages: English
• ISSNs: 1364-0321

Abstract

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. Highlights: Battery ageing mechanisms and the most common stress factors are discussed. Data-driven technologies for battery SOH estimations are summarized regarding the benefits and drawbacks. Data-driven health prediction methods including analytical models with data fitting, and machine learning methods are reviewed. A compilation of the existing issues and challenges in this field is given. Feasible and cost-effective solutions are suggested toward the improvement of related data-driven technologies.