Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells

By Xing Jin, A. Vora, Vaidehi Hoshing, Tridib Saha, G. Shaver, R. Edwin García, O. Wasynczuk, Subbarao Varigonda. 2017

145 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0fdefdceb38f1a187a443311c4de8d61d2324fad

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): “Although lithium-ion batteries in practice work under changeable currents, temperatures, and SOCs, the capacity degradation trends are expected to be similar under different working conditions [32], [33].”