Life prediction of batteries for selecting the technically most suitable and cost effective battery
By H. Wenzl, I. Baring-Gould, R. Kaiser, Bor Yann Liaw, Per Lundsager, J. Manwell, Alan Ruddell, V. Svoboda. 2005
193 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/3ca64940cf740bf65f1d66d4e6a669131382d51f
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
- Maik Naumann
- Holger C. Hesse
- Andreas Jossen
- Maitane Berecibar
- Aoife M. Foley
- Elise Nanini-Maury
- J. Van Mierlo
In Electrical and Electronic Engineering
- Maik Naumann
- Holger C. Hesse
- Kailong Liu
- Andreas Jossen
- Maitane Berecibar
- Aoife M. Foley
- Elise Nanini-Maury
- J. Van Mierlo
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Economic Optimization of Component Sizing for Residential Battery Storage Systems (2017, background): “This percentage value also defines the time of battery replacement: EOL→ SOHt=EOL ≤ αReplace.”
- Economic Analysis of the Dynamic Charging Electric Vehicle (2015, methodology): “The battery life prediction model adopted in this paper accumulates losses of battery life by discharge cycles [30], [32].”
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “10 ageing processes has to be focused on those with the greatest impact [24], this should be considered when designing the experimental testing scheme.”