Realistic lifetime prediction approach for Li-ion batteries
By E. Sarasketa-Zabala, E. Martinez-Laserna, Maitane Berecibar, I. Gandiaga, L. M. Rodriguez-Martinez, I. Villarreal. 2016
174 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/87022bc06d970e80a2d14aa170f9516d8f163edb
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
- Kailong Liu
- Maitane Berecibar
- Aoife M. Foley
- Elise Nanini-Maury
- J. Van Mierlo
- Harry Ernst Hoster
- Yi Li
- L. Saw
In Electrical and Electronic Engineering
- Kailong Liu
- Maitane Berecibar
- Aoife M. Foley
- Elise Nanini-Maury
- J. Van Mierlo
- Harry Ernst Hoster
- Zuansi Cai
- Yi Li
Citations in the corpus
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Most of the existing studies model the battery cyclic and calendar aging independently, and combine the two to make predictions under a dynamic load profile [6,94,95].”
- Novel thermal management system using mist cooling for lithium-ion battery packs (2018, background): “In extreme conditions, high operating temperature may cause irreversible chemical reactions, leading to the generation of thermal runaway [4-6].”