Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

By Datong Liu, Jingyue Pang, Jianbao Zhou, Yu Peng, Michael G. Pecht. Microelectronics and reliability, 2013

419 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7f8a48cabeee8b61d0c7bc27b818e923bf6dfcc1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. S. M. Kolake
2. Jing Lin
3. Michael A. Osborne
4. Piyush Tagade
5. Michael G. Pecht
6. S. Doo
7. Yaguo Lei
8. David A. Howey

In Automotive Engineering

1. Kailong Liu
2. Michael G. Pecht
3. David A. Howey
4. Christopher Hendricks
5. Nicholas Williard
6. Robert R. Richardson
7. Michael A. Osborne
8. Wei He

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. David A. Howey
8. Robert R. Richardson

Citations in the corpus

• 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.”
• Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, neutral): “[46] constructed a battery data Gaussian process.”
• Gaussian process regression for forecasting battery state of health (2017, methodology): “Lastly, it is [10] and used with the explicit mean function GP (Fig.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “[351] improved long-term prediction performance of GPR by combining two covariance functions to capture the actual trends of both global degradation and local regeneration.”
• Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017, neutral): “GPs have previously been used in relatively few studies on battery diagnostics/prognostics [25], [26], [27], however, they possess a variety of desirable attributes.”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[141] utilized a combination of covariance functions and mean functions in GPR for multi-step-ahead prognostics.”
• A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “Alternate applications of machine learning approaches for battery state estimation include particle swarm optimization [46], Gaussian process regression [47], recurrent neural fuzzy systems [48], genetic algorithm [49], sample entropy based approaches [50,51], the naive Bayes model [52,53] and other”