The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“While conventional Backpropagation (BP) neural networks handle nonlinear inputs effectively, they struggle with temporal relationships in timeseries data due to gradient issues [29][30][31].”
From SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm (2025) · cites this paper as [31]
Citations
Cites
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
The development and future of lithium ion batteries
Blomgren, G. E. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0251701jes · Cited by 1,940
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
The environmental impact of Li-Ion batteries and the role of key parameters – A review
Peters, J. F., Baumann, M., Zimmermann, B., Braun, J., & Weil, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.08.039 · Cited by 856
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732
Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 631
Remaining useful life prediction of lithium-ion battery with unscented particle filter technique
Miao, Q., Xie, L., Cui, H., Liang, W. Z., & PECHT, M. G. (2012) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 479
Direct Remaining Useful Life Estimation Based on Support Vector Regression
Khelif, R., Chebel‐Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Noureddine Zerhouni, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2623260 · Cited by 440
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794997 · Cited by 407
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–443 of 443
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
A Review of the Research on Remaining Useful Life Prediction Methods for Lithium-Ion Batteries
Feng, C., Huang, H., Lu, G., Zhai, J. P., & Zhao, Z. (2023) · doi:10.1109/epee59859.2023.10352063 · Cited by 1
Liu, Y., Zheng, Y., Chen, J., CHeng, Y., & Yan, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4823053 · Cited by 1
Prediction Models of Traffic Flow Driven Based on Multi-Dimensional Data in Smart Traffic Systems
Liu, H. (2020) · Springer eBooks · doi:10.1007/978-981-15-2837-8_7
Multisource Inverter Based Energy Management System in Electric Vehicle
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1158.0982s1119
A Novel Pattern- driven Stochastic Process for End-of-Life Forecasting
Zhang, Z., & Lee, C. G. (2021) · IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR) · doi:10.19124/ima.2021.01.2
Remaining useful life prediction of lithium-ion batteries with limited historical dataPreprint
Wnag, C., & Luan, W. (2021) · doi:10.46855/energy-proceedings-7275
Hua, Y., Jin, X., & Xie, S. (2022) · Processes · doi:10.3390/pr10122525
A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries
Akarslan, E., & Çınar, S. M. (2022) · Journal of Materials and Mechatronics A · doi:10.55546/jmm.1193510
State of Health Estimation of Lithium-Ion Battery Considering Random Charging
Ma, W., Shen, J., Gao, C., Chen, Z., & Liu, Y. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1027-4_22
Capacity Prediction for Lithium-Ion Batteries Using Different Neural Networks Methods
Loukili, S. E., Ezzati, A., Alla, S. B., & Zraibi, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47672-3_6
Liu, Y., Zheng, Y., Chen, J., Yan, L., & CHeng, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4753968
Review of Remaining Useful Life for Lithium-Ion Batteries
Hao, Z., He, Y., Chen, J., Luo, S., Zhou, X., & Duo, W. (2023) · Asia Power and Electrical Technology Conference (APET) · doi:10.1109/apet59977.2023.10489775
Joint Prediction of Capacity and Rul for Lithium-Ion Batteries Based on Ceemdan-Sos-Vmd-LstmPreprint
Zhang, Q., Feng, S., Liu, J., Xie, J., & Chen, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4845113
一种基于格拉米安角场和深度学习模型的锂离子电池健康状态预测新方法Preprint
zhao, Y., Geng, L., Hu, X., Wu, C., Du, Z., Shan, S., & Wei, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871059
Prediction of State-of-Health of Lithium-Ion Battery Based on Ceemdan-Sg-Lstm Combined ModelPreprint
Li, X., Huilin, Y., Wang, J., Xia, Y., Zheng, H., & Song, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4904232
Self-citationWang, H., Deng, Y. W., Hou, R. X., & Liu, Q. (2024) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls61622.2024.10606755
Keren Persis, P., Balakrishnan, V., Prabhu, G. A., Bommisetty, J., Sangameswaran, U., & Tharaniya, P. (2024) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn63059.2024.10847371
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
Lei, X., Xu, Y., Gong, X., & Li, Y. (2026) · Batteries · doi:10.3390/batteries12070251
Liu, H., Cheng, F., & Li, Y. (2025) · Springer eBooks · doi:10.1007/978-981-96-7218-9_1
Huang, J., Chen, B., Ma, X., Zhou, Y., Zhang, M., & Ma, J. (2024) · International Conference on Reliability, Maintainability and Safety · doi:10.1109/icrms63553.2024.00106
Gregor, M., Bauder, M., de Oliveira, A. K. V., Mast, P., Rüther, R., & Schweiger, H. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj17010003
Tefera, E. T., Qiao, H., Moses, A., Juanyun, L., Alberto, A., Xuefeng, W., & Chen, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5848172
Goldammer, E., Franke, M., Neupert, S., & Kowal, J. (2026) · Elsevier eBooks · doi:10.1016/b978-0-12-819987-9.00005-6
Machine-learning-based predictive maintenance for lithium-ion batteries in electric vehicles
Jorge, I. (2023) · theses.fr (ABES) · doi:10.70675/0c919879z7f18z49a1z88e9z40b21afc8345
Feng, Z., Saputra, D., Elias, F. H., Sturesson, T., & Löwe, W. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472440
Sahdev, G., Agarwal, S., Ojha, E., Kumari, R., & Bhati, P. (2026) · AIP conference proceedings · doi:10.1063/5.0340418
Battery capacity and health prediction using GA-optimized neural networks
Durmuş, F., & Karagöl, S. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12277-8
Özcan, R., & Koca, F. (2026) · Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi. · doi:10.69560/cujast.1904437
Manufacturing of CLAD-Based Battery Interconnects for Electric Vehicle Applications
Asundi, T., Date, P. P., & Rao, V. R. (2026) · Transactions of the Indian Institute of Metals · doi:10.1007/s12666-026-03919-5
Hadji, F., Hadji, A., Belfennache, D. E., Yekhlef, R., Fatmi, M., Alanazi, F. K., Yaylacı, M., & Abualreish, M. J. A. (2026) · Journal of Electroanalytical Chemistry · doi:10.1016/j.jelechem.2026.120421
Lithium-ion battery state of health estimation via a bayesian-gated triple-branch mamba model
Wang, H., Huang, Q., Liu, X., Guo, Y., & Xu, Z.-C. (2026) · Ionics · doi:10.1007/s11581-026-07305-5
Xiao, Z., Yang, J., Li, W., Gao, Y., & Wang, C. (2026) · Measurement and Control · doi:10.1177/00202940261468512
Zraibi, B., Lammamri, O., Loukili, S. E., & Said, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-11411-2_4
Xu, J., & Li, H. (2026) · Electronics · doi:10.3390/electronics15163687
Li, Z., Tian, X., Du, H., Xia, L., & Han, J. H. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18884-y
Lu, J., Huo, X., Guo, M., Dong, H., & Wang, K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124823
Li, J., Wang, H., Xu, Y., & Ngui, W. K. (2026) · International Journal of Automotive and Mechanical Engineering · doi:10.15282/ijame.23.3.2026.16.1052
Eswaran, M. S., Jaidev, R. S., Rehamathulla, R. M., & Suguna, G. (2026) · International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC) · doi:10.1109/icsseecc69062.2026.11722288
Liu, B., Zhen, W., & Huang, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7610919
Wang, R., Xingyu, H., Shi, Q., Wang, K., Wei, W., Zhang, M., Wang, N., Dong, J., Li, H., Xu, C., et al. (2026) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2026.100476
Yang, D., 冯春, Cao, J., Lu, J., Tang, Z., Wei, W., & Wang, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7624745
Metadata
- Authors
- Li, X., Zhang, L., Wang, Z., & Dong, P.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- Zhang, Lei
- Wang, Zhenpo
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 21
- Pages
- 510-518
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W2907460853
- Cited by
- 445
- References
- 36
- Citations by year
- 2026: 29 · 2025: 59 · 2024: 71 · 2023: 85 · 2022: 66 · 2021: 75 · 2020: 38 · 2019: 22
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Energy Storage (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1764258)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- lithium-ion batteries, remaining useful life prediction, long short-term memory, Elman neural network, battery capacity degradation, recurrent neural networks
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