Dr. habil. Eng. Mihai V. MICEA Professor Head Department
Coordinator of the DSPLabs
Politehnica University of Timisoara
Faculty of Automation and Computers
Department of Computer and Information Technology
DSPLabs: Digital Signal Processing Laboratories

2, Vasile Parvan Blvd. , 300223, Timisoara, Romania
Tel/Fax: +40 256 403271 /+40 256 403214
E-mail: mihai.micea<at>cs.upt.ro
Webpage: http://dsplabs.cs.upt.ro/~micha
Citations Report
 
 
Cited Paper
[Jrnl 1]Online state of health prediction method for lithium-ion batteries, based on gated recurrent unit neural networks
Author(s): Ungurean, Lucian; Micea, Mihai Victor; Carstoiu, Gabriel
In: International Journal of Energy Research
Volume 44, Issue 8
Editor(s): Dincer, Ibrahim
Publisher: Wiley
USA, Jun. 2020
Pages: 6767 - 6777, ISSN 0363-907X, DOI: 10.1002/er.5413
Indexed: ISI Web of Science, Thomson Reuters (WOS: 000524334200001), IF: 5.164
109
20 Citations in total (with a cummulated IF: 100.325)
 
 
Citing papers
[Jrnl 20]Z. Chen, H. Zhao, Y. Zhang, S. Shen, J. Shen, Y. Liu, "State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network", J Power Sources, vol. 521, Elsevier Science B.V., The Netherlands, Dec. 2021, ISSN 0378-7753, DOI: 10.1016/j.jpowsour.2021.230892. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 19]X. Shu, S. Shen, J. Shen, Y. Zhang, . et al., "State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives", Iscience, vol. 24 (11), Cell Press, USA, Nov. 2021, ISSN 2589-0042, DOI: 10.1016/j.isci.2021.103265. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 18]E. Vanem, C. B. Salucci, A. Bakdi, O. A. Alnes, "Data-driven state of health modelling-A review of state of the art and reflections on applications for maritime battery systems", J Energy Storage, vol. 43, Elsevier Science B.V., The Netherlands, Nov. 2021, ISSN 2352-152X, DOI: 10.1016/j.est.2021.103158. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 17]L. Zhang, S. Wang, C. Zou, . et al., "A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence", Int. J Energy Res., vol. 45 (12), Wiley, USA, Oct. 2021, pp. (17858 - 17877), ISSN 0363-907X, DOI: 10.1002/er.6930. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 16]A. Yuliani, A. Ramdan, V. Zilvan, A. Supianto, D. Krisnandi, R. Yuwana, D. Prajitno, H. Pardede, "Remaining Useful Life Prediction of Lithium-Ion Battery Based on LSTM and GRU", Int. C Comput. Contr. Informatics Applic., ACM, USA, Oct. 2021, pp. (21 - 25), ISBN 978-1-4503-8524-4, DOI: 10.1145/3489088.3489092. [Indexed: Scopus, Elsevier].
[Jrnl 15]M. Adaikkappan, N. Sathiyamoorthy, "Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review", Int. J Energy Res., vol. 46 (3), Wiley, USA, Oct. 2021, pp. (2141 - 2165), ISSN 0363-907X, DOI: 10.1002/er.7339. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 14]X. Lai, Y. Huang, H. Gu, . et al., "Turning waste into wealth: A systematic review on echelon utilization and material recycling of retired lithium-ion batteries", Energy Storage Mater., vol. 40, Elsevier Science B.V., The Netherlands, Sep. 2021, pp. (96 - 123), ISSN 2405-8297, DOI: 10.1016/j.ensm.2021.05.010. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 13]R. Rouhi Ardeshiri, C. Ma, "Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach", Int. J Energy Res., vol. 45 (11), Wiley, USA, Sep. 2021, pp. (16633 - 16648), ISSN 0363-907X, DOI: 10.1002/er.6910. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 12]Y. Hou, Z. Zhang, P. Liu, C. Song, Z. Wang, "Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles", Adv. Mech. Eng., vol. 13 (7), Sage Publications, London, UK, Jul. 2021, ISSN 1687-8132, DOI: 10.1177/16878140211027735. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 11]L. Zheng, Y. Hou, T. Zhang, X. Pan, "Performance prediction of fuel cells using long short-term memory recurrent neural network", Int. J Energy Res., vol. 45 (6), Wiley, USA, May. 2021, pp. (9141 - 9161), ISSN 0363-907X, DOI: 10.1002/er.6443. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 10]M. S. H. Lipu, M. A. Hannan, T. F. Karim, . et al., "Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook", J Clean Prod., vol. 292, Elsevier Science B.V., The Netherlands, Apr. 2021, ISSN 0959-6526, DOI: 10.1016/j.jclepro.2021.126044. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 9]C. Jiang, Y. Mao, V. Chai, M. Yu, "Day-ahead renewable scenario forecasts based on generative adversarial networks", Int. J Energy Res., vol. 45 (5), Wiley, USA, Apr. 2021, pp. (7572 - 7587), ISSN 0363-907X, DOI: 10.1002/er.6340. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 8]K. Kaur, A. Garg, X. Cui, S. Singh, B. K. Panigrahi, "Deep learning networks for capacity estimation for monitoring SOH of Li-ion batteries for electric vehicles", Int. J Energy Res., vol. 45 (2), Wiley, USA, Feb. 2021, pp. (3113 - 3128), ISSN 0363-907X, DOI: 10.1002/er.6005. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 7]A. Chmielewski, J. Mozaryn, P. Piorkowski, J. Dybala, "Comparison of hybrid recurrent neural networks anddual-polarizationmodels of valve regulated lead acid battery", Int. J Energy Res., vol. 45 (2), Wiley, USA, Feb. 2021, pp. (2560 - 2580), ISSN 0363-907X, DOI: 10.1002/er.5947. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 6]C. Huang, Y. Shen, Y. Chen, H. Chen, "A novel hybrid deep neural network model for short-term electricity price forecasting", Int. J Energy Res., vol. 45 (2), Wiley, USA, Feb. 2021, pp. (2511 - 2532), ISSN 0363-907X, DOI: 10.1002/er.5945. [Indexed: ISI Web of Science, Clarivate Analytics].
[Jrnl 5]A. Kim, S. Lee, "Online State of Health Estimation of Batteries under Varying Discharging Current Based on a Long Short Term Memory", Int. C Ubiquitous Inf. Manag. Commun., IEEE, USA, Jan. 2021, ISBN 9-780-7381-0508-6, DOI: 10.1109/IMCOM51814.2021.9377368. [Indexed: Scopus, Elsevier].
[Jrnl 4]K. Yang, X. Wang, "Abnormal identification of lubricating oil parameters and evaluation of physical and chemical properties based on machine learning", IOP C Ser. Mater. Sci. Eng., vol. 1043 (5), IOP Publishing Ltd., USA, 2021, ISSN 1757-8981, DOI: 10.1088/1757-899X/1043/5/052053. [Indexed: Scopus, Elsevier].
[Jrnl 3]L. Yao, S. Xu, A. Tang, . et al., "A review of lithium-ion battery state of health estimation and prediction methods", World Electr. Veh. J, vol. 12 (3), MDPI AG., Basel, Switzerland, 2021, ISSN 2032-6653, DOI: 10.3390/wevj12030113. [Indexed: Scopus, Elsevier].
[Jrnl 2]S. Wang, Y. S. Fan, C. Fernandez, C. Yu, W. Cao, Z. Chen, "Battery System Modeling", Batt. Syst. Modeling, Elsevier, The Netherlands, 2021, pp. (1 - 347), ISBN 9-780-3239-0472-8, DOI: 10.1016/B978-0-323-90472-8.09993-5. [Indexed: Scopus, Elsevier].
[Jrnl 1]R. Li, H. Zhang, W. Li, X. Zhao, Y. Zhou, "Toward group applications: A critical review of the classification strategies of lithium-ion batteries", World Electr. Veh. J, vol. 11 (3), MDPI AG., Basel, Switzerland, 2020, pp. (1 - 23), ISSN 2032-6653, DOI: 10.3390/wevj11030058. [Indexed: Scopus, Elsevier].