Cited by 274Advanced Battery Technologies ResearchMachine Fault Diagnosis TechniquesReliability and Maintenance Optimization

An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Open access
Liu, Jie, Saxena, Abhinav, Goebel, Kai, Saha, Bhaskar, Wang, Wilson·2010·Annual Conference of the PHM Society

Abstract

Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.36001/phmconf.2010.v2i1.1896

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Hu et al. proposed using a genetic algorithm-based fuzzy C-means clustering technique to partition training data sampled from a lithium-ion battery during driving cycle-based test, and applied the clustering result to automatically learn the topology and antecedent parameters of the ANN model for battery state estimation.

    “Hu et al. [129] proposed to use novel genetic algorithm-based fuzzy C-means clustering technique to partition the training data sampled of a lithium-ion battery during the driving cycle-based test, and the clustering result was then applied to automatically learn the topology and antecedent parameters of the ANN model for battery state estimation.”

    From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [129]

    Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. K. Goebel, B. Saha, and A. Saxena (2008). A comparison of three data-driven techniques for prognostics, in Proceedings of the 62 nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT), pp. 119-131, Virginia Beach, VA, May 2008.
  2. J. Korbicz (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer, Berlin, 2004.
  3. M. Orchard, B. Wu, and G. Vachtsevanos (2005), "A Particle Filter Framework for Failure Prognosis", Proceedings of World Tribology Congress III, Washington DC, Sept. 12-16, 2005.
  4. H. Tong and K. S. Lim (1980). Threshold autoregression, limited cycles and cyclical data, Journal of the Royal Statistical Society, vol. 42, pp. 245-292, 1980.

Cited by

Sorted by citations

Authors
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W.
Affiliations
  • Carleton University (CA)
  • Ames Research Center (US)
  • Lakehead University (CA)
Venue
Annual Conference of the PHM Society
Publisher
PHM Society
Published
2010-10-10
Volume
2
Issue
1
ISSN
2325-0178 (ISSN-L)
OpenAlex
W2126450366
Cited by
274
References
29
Citations by year
2026: 7 · 2025: 17 · 2024: 23 · 2023: 29 · 2022: 21 · 2021: 29 · 2020: 30 · 2019: 25 · 2018: 18 · 2017: 16 · 2016: 15 · 2015: 10 · 2014: 11 · 2013: 12 · 2012: 7
FWCI
11.61
Type
conference paper
Language
en
Open access
Locations
Annual Conference of the PHM Society (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
Keywords
remaining useful life prediction, lithium-ion batteries, battery prognostics, maintenance scheduling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review