Critical review of state of health estimation methods of Li-ion batteries for real applications
By Maitane Berecibar, I. Gandiaga, I. Villarreal, Noshin Omar, J. Van Mierlo, P. Bossche. 2016
964 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ac74da453d5bdeb7bd46e70569b7cc0a17d0d9dd
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
- Carlos Vidal
- Pawel Malysz
- Maitane Berecibar
- P. Kollmeyer
- Ali Emadi
- Aoife M. Foley
- Elise Nanini-Maury
In Electrical and Electronic Engineering
- Kailong Liu
- Carlos Vidal
- Pawel Malysz
- P. Kollmeyer
- Ali Emadi
- Maitane Berecibar
- Aoife M. Foley
- Elise Nanini-Maury
Citations in the corpus
- A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “For model-based method, on the one hand, the battery capacity or internal resistance is taken as the time-varying parameters based on the battery equivalent circuit model [15] and electrochemical model [61].”
- A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, background): “As one key function of the BHDS, a well-designed future capacities and RUL prediction strategy should not only predict the battery capacity variation but also report the uncertainty level of predicted values, further enabling EV users to make reasonable decisions to avoid unexpected failures and los”
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “(2016) [19] General review on SOH estimation methods and ageing mechanism diagnosis tools Farmann et al.”