Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

By A. Nuhic, Tarik Terzimehić, T. Soczka-Guth, M. Buchholz, Klaus C. J. Dietmayer. 2013

610 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/3e88122f84be8c9b70a87e7d034ca698110cbb31

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. Kailong Liu
2. Michael G. Pecht
3. Yong Guan
4. Lifeng Wu
5. Xiaohui Fu
6. David A. Howey
7. Rui Xiong
8. Hongwen He

In Electrical and Electronic Engineering

1. Kailong Liu
2. David A. Howey
3. Michael G. Pecht
4. Rui Xiong
5. Hongwen He
6. Dirk Uwe Sauer
7. Weihan Li
8. Yongzhi Zhang

In Safety, Risk, Reliability and Quality

1. Kailong Liu
2. Yong Guan
3. Lifeng Wu
4. Xiaohui Fu
5. Michael G. Pecht
6. Kwok Leung Tsui
7. David A. Howey
8. Michael A. Osborne

Citations in the corpus

• A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014, methodology): “Recent examples of applying SVM in RUL prediction in the literature include [1,39,40].”
• Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, neutral): “To produce battery SOH estimates, this class of differential signal requires further manipulation via classification and/or regression techniques, e.g., SVM in [40].”
• Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, methodology): “[14] used SVM to embed diagnosis and prognostics of system health with an aim to estimate the state of health (SOH) and RUL of lithium-ion”
• Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2022, neutral): “Regarding the prognostics of battery health, Nuhic et al. [14] explored applying Support Vector Machine (SVM) to learn the decay pro-The pro-The associate editor coordinating the review of this manuscript and approving it for publication was Zhe Zhang . cess.”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “A similar work has been reported for lithium-ion battery prognosis in which the loss of rated capacity was used as the HI of battery, with SVM as the ML technique [118].”
• Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis (2024, positive): “Commonly used data-drivenmethodsincludelinearregression 25 ,support vectormachines 26 , Gaussian process regression 27 , deep neural networks 28,29 , etc. Xia et al. 30 extracted features from incremental capacity (IC) curves and differential voltage (DV) curves to estimate SOH.”
• Impact of the electricity mix and use profile in the life-cycle assessment of electric vehicles (2013, background): “The battery state of health is greatly influenced by the load and environmental conditions [49,50].”