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 language | English |
|---|---|
| Article number | 109254 |
| Number of pages | 18 |
| Journal | Renewable and Sustainable Energy Reviews |
| Volume | 113 |
| Early online date | 12 Jul 2019 |
| DOIs | |
| Publication status | Published - 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
Access to Document
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review
© 2019 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/,which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.