Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide + spinel manganese oxide positives: Part 1, aging mechanisms and life estimation

By John Wang, J. Purewal, Ping Liu, Jocelyn Hicks-Garner, Souren Soukazian, E. Sherman, Adam Sorenson, Luân Vũ, Harsh Tataria, M. Verbrugge. 2014

488 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cfb90c9585a23bea2f7fee1ec4da4d9b15b028e0

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. Rui Xiong
2. Hongwen He
3. Yongzhi Zhang
4. Michael G. Pecht
5. Guangcai Zhao
6. Yandan Tan
7. Xiaosong Hu
8. Xianke Lin

In Electrical and Electronic Engineering

1. Hongwen He
2. Rui Xiong
3. Yongzhi Zhang
4. Michael G. Pecht
5. Guangcai Zhao
6. Yandan Tan

Citations in the corpus

• Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, background): “These anomalies can be caused by the specific degradation mechanisms or manufacturing variability [31].”