Comparison of prognostic algorithms for estimating remaining useful life of batteries

By Bhaskar Saha, Kai Goebel, Jon Christophersen. 2009

432 citations recorded by the source corpus.

Subfields: Automotive Engineering, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7c73266ff900ed9a63b72a0cfc26e12b28cc6167

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. Min Xie
3. Michael G. Pecht
4. Xiaosheng Si
5. Jooho Choi
6. Lei Ren
7. Dawn An
8. Li Zhao

In Biomedical Engineering

1. A. K. Qin
2. Min Xie
3. Z. Ye
4. Nam-Ho Kim

In Control and Systems Engineering

1. Michael G. Pecht
2. Changhua Hu
3. Donghua Zhou
4. Wenbin Wang
5. Xiaosheng Si
6. Min Xie
7. Z. Ye
8. Robert X. Gao

Citations in the corpus

• Remaining useful life prediction of lithium-ion battery with unscented particle filter technique (2013, methodology): “Saha presented a comparative study of autoregressive integrated moving average (ARIMA), extended Kalman filtering, relevance vector machine (RVM), support vector machine (SVM) and PF approaches on experimental data collected from lithium-ion batteries in the remaining useful life prediction [19].”
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, background): “On the other hand, a relatively small number of reviews on prognostics are available [13–17].”
• Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “for RUL estimation, see Saha et al. (2009). All of the above filteringbased models have one drawback in common in that they need a”
• Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, methodology): “Implementation of traditional data-driven methods [4], [6]–[12], such as neural networks (NNs) [7] [83], support vector machines (SVMs) [6], local Gaussian regres-”
• Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach (2018, methodology): “we adopt the lithium-ion battery batteries data set from NASA AMES Center [38], [39].”
• A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (2017, methodology): “For example, model-based prognostics include methods based on Wiener and Gamma processes [13], hidden Markov models (HMMs) [14], Kalman filters [15,16], and particle filters [17–20].”
• Stochastic modelling and analysis of degradation for highly reliable products (2015, methodology): “Variants of the Kalman filter, such as the extended Kalman filter [101] and the unscented Kalman filter, should be used.”