Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries

By A. Schmidt, M. Bitzer, A. Imre, Lino Guzzella. 2010

137 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/70f605b8b9a3355706f5c577ee3a1397b9b1af33

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

1. Michael G. Pecht
2. Kwok Leung Tsui
3. Yinjiao Xing
4. Lino Guzzella
5. Eden W. M. Ma
6. Philipp Elbert
7. Søren Ebbesen
8. Z. Ye

In Electrical and Electronic Engineering

1. Kwok Leung Tsui
2. Michael G. Pecht
3. Lino Guzzella
4. Eden W. M. Ma
5. Yinjiao Xing
6. Simona Onori
7. G. Suri

Citations in the corpus

• An ensemble model for predicting the remaining useful performance of lithium-ion batteries (2013, methodology): “…Kong, Kowloon, Hong K E-mail addresses: yxing3@student.cityu.edu.hk (Y. du.hk (E.W.M. Ma), kltsui@cityu.edu.hk (K.-L. Tsui Pecht). a b s t r a c t We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries.”
• Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles (2012, background): “Such observers are already available [10] and [27].”