Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

By Qiang Miao, Lei Xie, Hengjuan Cui, Wei Liang, Michael G. Pecht. Microelectronics and reliability, 2013

452 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/c09f58f7e0d8118ebf0dfd8f498739f43c12ecfa

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. Z. Ye
2. Xianke Lin
3. Michael G. Pecht
4. Xiaosong Hu
5. Simona Onori
6. Yunhong Che
7. Min Xie
8. Christopher Hendricks

In Control and Systems Engineering

1. Xiaosong Hu
2. Xiaosheng Si
3. Xianke Lin
4. Simona Onori
5. Yunhong Che
6. Wei He
7. Nicholas Williard
8. Christopher Hendricks

In Safety, Risk, Reliability and Quality

1. Z. Ye
2. Min Xie
3. Xiaosong Hu
4. Michael G. Pecht
5. Xianke Lin
6. Xiaosheng Si
7. Simona Onori
8. Yong Guan

Citations in the corpus

• A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “Some recent modifications to the PF algorithm are also exploited for battery state estimation, like regularized auxiliary PF in [30], unscented PF in [31] and GaussHermite PF in [32].”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “The basic idea of these methods is to fit the capacity loss [13] or internal”
• Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, unknown): “[19] combine the degradation modeling and an”
• Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability (2013, methodology): “Prediction algorithms [34–41] could further supplement these techniques to provide accurate time-to-failure estimations.”