The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Open accessAbstract
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.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
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.
Citations
Cites
ANFIS: adaptive-network-based fuzzy inference system
Jang, J. R. (1993) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.256541 · Cited by 16,304
Multivariate Adaptive Regression Splines
Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,108
A review on machinery diagnostics and prognostics implementing condition-based maintenance
Jardine, A. K. S., Lin, D., & Banjević, D. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.012 · Cited by 4,861
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
Jang, J. R., & Sun, C. (1996) · Cumulative Index of Computer Aided Architectural Design · Cited by 4,629
Theory and Practice of Recursive Identification
Lennart Ljung, A., & Söderström, T. (1983) · Lund University Publications (Lund University) · Cited by 3,989
Friedman, J. H., & Stuetzle, W. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477729 · Cited by 2,116
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
Self-citationDynamic lithium-ion battery model for system simulation
Gao, L., Liu, S., & Dougal, R. A. (2002) · IEEE Transactions on Components and Packaging Technologies · doi:10.1109/tcapt.2002.803653 · Cited by 797
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
Self-citationMetrics for Offline Evaluation of Prognostic Performance
Saxena, A., Celaya, J. ́. R., Saha, B., Saha, S., & Goebel, K. (2021) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2010.v1i1.1336 · Cited by 449
Self-citation
Research works printed but not linked: 4
- 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.
- J. Korbicz (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer, Berlin, 2004.
- 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.
- 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
Items 201–274 of 274
Early Identification of Battery End-of-Life Using Machine Learning
Tjandra, R., Jiang, H., Zainudin, E. B., Yasmin, M. I. B., Soh, C. B., Feng, E., Soh, D. C. L., Cao, S., & Tan, K. T. (2023) · doi:10.1109/cpeee56777.2023.10217699 · Cited by 4
Li, Y., Li, L., Liu, R., & Pan, E. (2024) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-024-09581-7 · Cited by 4
Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery
Rahman, M. A., Anwar, S., & Izadian, A. (2016) · Advances in Automobile Engineering · doi:10.4172/2167-7670.1000159 · Cited by 4
VTB-RTRRP: Variable Threshold Based Response Time Reliability Real-Time Prediction
Jin, C., Kang, R., & Li, R. (2017) · IEEE Access · doi:10.1109/access.2017.2741666 · Cited by 4
Accurate RUL Prediction Based on Sliding Window with Sparse Sampling
Song, C., & Lee, S. (2021) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom51814.2021.9377402 · Cited by 4
Liu, J., Thelen, A., Hu, C., & Yang, X. (2021) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2021.v13i1.3053 · Cited by 4
Self-citationSun, H., Li, Y., & Hou, Y. (2021) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess52771.2021.9693652 · Cited by 4
Li-ion Battery Life Estimation using K-Nearest Neighbor Algorithm
Allirani, S., Pooja, K., Soundarya, E., & Nair, S. S. (2023) · International Conference on Edge Computing and Applications (ICECAA) · doi:10.1109/icecaa58104.2023.10212304 · Cited by 4
Data-driven on-line health assessment for lithium-ion battery with uncertainty presentation
Song, Y., Liu, D., & Peng, Y. (2018) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2018.8448707 · Cited by 4
Lithium-Ion Battery State of Health Estimation Based on LSTM-Transformer
Meng, F., Wang, P., & Wang, J. (2024) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt60213.2024.10545710 · Cited by 3
Hespeler, S. C., & Fuqua, D. (2021) · Journal of Energy and Power Technology · doi:10.21926/jept.2101003 · Cited by 3
Health diagnosis of lithium-ion battery cell using vibration-based test and analysis
Pham, H. L. (2013) · Cited by 3
Stochastic Model for Lithium Ion Battery Lifecycle Prediction and Parametric Uncertainties
Ossai, C. I., & Raghavan, N. (2018) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/ram.2018.8463112 · Cited by 3
Mchara, W., Khalfa, M. A., & Manai, L. (2024) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa63423.2024.10912566 · Cited by 2
Ramdan, A., Zilvan, V., Yuliani, A. R., Pardede, H. F., Hadiana, A., Suryawati, E., Ihsan, C. N., Yuwana, R. S., Rozie, A. F., & Sugiarto, B. (2025) · Journal of King Saud University - Engineering Sciences · doi:10.1007/s44444-025-00035-5 · Cited by 2
Nagi, I., Yin, D., Yousafzai, A., Tzannetos, D., Mengshoel, O. J., Martin, R. A., & Kulkarni, C. S. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-0685 · Cited by 2
Accelerated State-of-Health Estimation for Battery Recycling, using Neural Networks
Speckmann, F.-W., Ströbel, M., & Birke, K. P. (2020) · International Conference on Power and Energy Applications (ICPEA) · doi:10.1109/icpea49807.2020.9280134 · Cited by 2
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics
Shinde, G., Mohapatra, R., Krishan, P., & Sengupta, S. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386134 · Cited by 2
Optimized Data-Driven Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Ansari, S., Ayob, A., Hussain, A., Lipu, M. S. H., Saad, M. H. M., & Hannan, M. A. (2023) · IEEE International Conference on Energy Technologies for Future Grids (ETFG) · doi:10.1109/etfg55873.2023.10407420 · Cited by 2
Adithya, C. H., Hegde, A. R., & Prasad, S. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543402 · Cited by 2
Wang, B., Li, S., Ding, Z., Yang, L., & Zhang, Y. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10661618 · Cited by 2
Li, Y., Liu, C., Ni, H.-P., Paul, F., Chong, W. O., & Chou, J. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112349 · Cited by 2
Data-driven approach for the prediction of remaining useful life
Xie, G., Li, X., Zhang, C., Hei, X., Qian, F., Hu, S., Cao, Y., & Cai, B. (2017) · IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications · doi:10.1109/mape.2017.8250820 · Cited by 2
DEEP NEURAL NETWORKS FOR PREDICTIVE MAINTENANCE
Sai, V. K. R. (2019) · Modelirovanie, optimizaciâ i informacionnye tehnologii. · doi:10.26102/2310-6018/2019.27.4.011 · Cited by 2
Prognóstico de falhas baseado em redes neurais com estados de eco
Simeón, E. J. A. (2015) · doi:10.26512/2015.08.t.20510 · Cited by 2
Development of data-driven method for capacity estimation and prognosis for lithium-ion batteries
Koul, A. (2020) · doi:10.32657/10356/137313 · Cited by 2
Reliability Estimation for Relay Protection Devices with Long Short-Term Memory Recurrent Network
Zhou, J., Dai, B., Wang, R., Liu, S., Jiang, Z., & Chen, G. (2023) · International Conference on Power System Technology (POWERCON) · doi:10.1109/powercon58120.2023.10331285 · Cited by 1
A smart remaining battery life prediction based on MARS
Xi, X., Xu, W., Bai, X., Rui, X., Wang, H., Forster, J., Wang, Y., Zhao, X., Kong, X., & Liang, T. (2014) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2014.6816399 · Cited by 1
Probabilistic Model-Based Prognostics Using Meshfree Modeling
Ekwaro-Osire, S., Endeshaw, H. B., Alemayehu, F. M., & Gecgel, O. (2017) · Springer eBooks · doi:10.1007/978-3-319-55852-3_5 · Cited by 1
Schlegel, B., Mrowca, A., Wolf, P., Sick, B., & Steinhorst, S. (2018) · IEEE International Conference on Big Data Analysis (ICBDA) · doi:10.1109/icbda.2018.8367659 · Cited by 1
Quantifying the Impact of Prognostic Distance on Average Cost per Cycle
Bhattacharya, S., Fiondella, L., Saxena, S., & PECHT, M. G. (2019) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2019.8819408 · Cited by 1
Data-Driven Prognostics of Lithium-Ion Rechargeable Battery using Bilinear Kernel Regression
Hubbard, C., Bavlsik, J., Hegde, C., & Hu, C. K. (2016) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2016.v8i1.2550 · Cited by 1
A State of Power Based Deep Learning Model for State of Health Estimation of Lithium-Ion Batteries
Shin, J., Joe, I., & Hong, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-90321-3_77 · Cited by 1
Prognostic Algorithm for Remaining Useful Life of Battery Using Particle Filtering
Ali, B. M., & Qifu, F. A. N. (2021) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp54369.2021.9611926 · Cited by 1
Remaining Useful Life Prediction Based on CNN Method
Wei, C., Yue, S., & Bai, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-3927-3_30 · Cited by 1
Modelling of Li-Ion battery state-of-health with Gaussian processes
Dudek, A., & Baranowski, J. (2023) · Archives of Electrical Engineering · doi:10.24425/aee.2023.146042 · Cited by 1
Zhang, Z., Liu, P., Ge, W., & Lai, Y. (2023) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3613330.3613333 · Cited by 1
Zhu, Y., Gu, X., Mao, Z., Zhao, W., & Shang, Y. (2023) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ad0492 · Cited by 1
Residual Life Prediction of Lithium Battery Based on the Improved Vector Regression Algorithm
Liu, B., Du, X., Wang, S., Wang, Y., Liang, G., & Dong, X. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-7047-2_41 · Cited by 1
Li, J., Jin, J., Chen, H., & Huang, W. (2024) · International Conference on Signal Processing · doi:10.1109/icsp62129.2024.10846622 · Cited by 2
Nonlinear Mixed Effect Model-Based Prognostics for Lithium-ion Battery Charge Decay
Ossai, C. I., & Raghavan, N. (2018) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/ram.2018.8463128 · Cited by 1
Lee, J.-H., Kim, H., & Lee, I.-S. (2020) · International Journal of Engineering Research and Technology · doi:10.37624/ijert/13.11.2020.3240-3246 · Cited by 1
Identifying Parameters for Nonlinear Degradation-Based Prognostics
Yang, F., & Salahuddin, M. (2018) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-6989-3.ch012
On-Board Prediction of Remaining Useful Life of Lithium-Ion Battery
Hu, C. K., Shen, S., & Li, Y. (2019)
Zou, Q. (2020) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc49476.2020.9353159
Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosisPreprint
Greenbank, S., & Howey, D. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07576
Research on Machine Learning Method for Equipment Health Management in Industrial Internet of Things
Tan, Z., & Wen, Y. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-2540-4_19
Statistical Aspects in Neural Network for the Purpose of Prognostics
An, D., Kim, N., & Choi, J.-H. (2014) · PHM Society European Conference · doi:10.36001/phme.2014.v2i1.1547
Self-Adaptive Ageing Models for Optimal Management and Planning of Assets in Microgrids
Coosemans, T., Parys, W., Cauwer, C. D., Berecibar, M., & Messagie, M. (2023) · Green energy and technology · doi:10.1007/978-3-031-33906-6_12
Deep Neural Network Estimation of Battery Cell Age
Kafadarova, N. M., Sotirov, S., Stoynova, A. V., Rizanov, S. M., & Bonev, B. B. (2023) · International Scientific Conference Electronics (ET) · doi:10.1109/et59121.2023.10279758
Battery lifetime prediction methods
Dai, H., & Zhu, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15543-7.00004-2
Sahay, R., Pugalenthi, K., & Raghavan, N. (2023) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-hangzhou58797.2023.10482631 · Cited by 1
Monitoring battery status and facility conditions
Tabassum, T., & Khalghani, M. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99545-0.00003-8
Венерович, З. А., Шайдуллин, В. Ф., & Khakimyanov, M. I. (2024) · International Ural Conference on Electrical Power Engineering (UralCon) · doi:10.1109/uralcon62137.2024.10718931
Life Prediction of Lithium-Ion Batteries Based on Fusion Techniques
Wu, P., Liu, P., Hu, X., Zhang, Z., Wang, X., & Ma, N. (2024) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac63745.2024.10899715
Nair, A., Bhatnagar, S., Srinivasan, K., & Manoov, R. (2024) · IEEE Conference on Engineering Informatics (ICEI) · doi:10.1109/icei64305.2024.10912340
An Integrated Knowledge Distillation Framework for Battery SOH Estimation
Yang, L., Li, C., Zhang, B., Xie, R., Mo, H., & Huang, J. (2025) · International Conference on Smart Grid and Green Energy (ICSGGE) · doi:10.1109/icsgge64667.2025.10984977
Crossformer Network for RUL Prediction of Lithium-Ion Batteries
Chang, L., Jiang, S., Tang, X., & Xu, G. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-97352-9_4
AutoDS: a de-stationary transformer method for lithium-ion battery remaining useful life prediction
Zhang, X., Chen, Z., Li, Y., Zhang, C., Chai, X., Ye, M., & Zhu, M. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04720-5 · Cited by 1
Yaman, Ö., & Yaman, N. N. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae0882
Zhang, L., Jiang, D., & Wang, J. (2026) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965415790251110094005
UAV mission planning based on Prognosis & Health Management
Schacht-Rodríguez, R. (2019) · doi:10.70675/c49c346azc454z4990z8636za2b78a78988c
Model-based and data-driven prediction methods for prognostics
Nguyen, H.-P. (2020) · theses.fr (ABES) · doi:10.70675/8d04d04bzf9b2z4805z85ffz1d688700d5f3
Competency-Based Decision-Making Framework for Predictive Maintenance in Aviation
Fedorov, R. A., & Pavlyuk, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19245-5_38
Sahdev, G., Agarwal, S., Ojha, E., Kumari, R., & Bhati, P. (2026) · AIP conference proceedings · doi:10.1063/5.0340418
Liu, J., She, C., Bin, G., Xu, Y., & Zhou, L. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123737
Self-citationEbrahimi, H., Mozafari, N., Arefi, M. M., Khayatian, A., & Luo, H. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105099
Patel, N., Shah, A., Shrotriya, A., & Vakharia, V. (2026) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon69332.2026.11629778
Prediction of State of Health and Remaining Useful Life for Li-Ion Battery Using Ensemble Learning
Tu, V. T., & Hoang, T. M. (2026) · International Conference on Green Energy and Applications (ICGEA) · doi:10.1109/icgea69553.2026.11642834
Metadata
- 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
- diamond
- doi.org
- Locations
- Annual Conference of the PHM Society (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- 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