Battery Lifetime Prognostics
By Xiaosong Hu, Le Xu, Xianke Lin, Michael G. Pecht. 2020
1,071 citations recorded by the source corpus.
Summary Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.
Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/62fa13ce896823cb2b6934540af47d64f6f3adf2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Widanalage Dhammika Widanage
- Xinhua Liu
- Shichun Yang
- Billy Wu
- Valentin Sulzer
- Anna G. Stefanopoulou
- David A. Howey
In Control and Systems Engineering
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “For lifetime estimation, these approaches are divided into physics based, data driven and hybrid approaches [24] .”
- The challenge and opportunity of battery lifetime prediction from field data (2021, background): “ture consider methods for state of health prediction [9–15], lifetime prediction [9, 14, 16, 17], and/or fusion of physics-based and data-driven models [17–19], but are historically restricted to fairly small datasets under very controlled conditions.”
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, neutral): “Inspired by biological systems, especially the human brain, ANNs are designed to mathematically imitate this process in the nonlinear problems [77], [78].”