EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 392 (2024) MATEC Web Conf., 392 (2024) 01178 References
Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01178
Number of page(s) 10
DOI https://doi.org/10.1051/matecconf/202439201178
Published online 18 March 2024
  1. S. Deep, S. Banerjee, S. Dixit, and N. I. Vatin, “Critical Factors Influencing the Performance of Highway Projects: Empirical Evaluation of Indian Projects,” Buildings, vol. 12, no. 6, Jun. 2022, doi: 10.3390/BUILDINGS12060849. [CrossRef] [Google Scholar]
  2. C. Shyamlal et al., “Corrosion Behavior of Friction Stir Welded AA8090-T87 Aluminum Alloy,” Materials, vol. 15, no. 15, Aug. 2022, doi: 10.3390/MA15155165. [CrossRef] [Google Scholar]
  3. G. Upadhyay et al., “Development of Carbon Nanotube (CNT)-Reinforced Mg Alloys: Fabrication Routes and Mechanical Properties,” Metals (Basel), vol. 12, no. 8, Aug. 2022, doi: 10.3390/MET12081392. [CrossRef] [Google Scholar]
  4. P. Singh et al., “Development of performance-based models for green concrete using multiple linear regression and artificial neural network,” International Journal on Interactive Design and Manufacturing, 2023, doi: 10.1007/S12008-023-01386-6. [Google Scholar]
  5. M. Makwana et al., “Effect of Mass on the Dynamic Characteristics of Single– and Double-Layered Graphene-Based Nano Resonators,” Materials, vol. 15, no. 16, Aug. 2022, doi: 10.3390/MA15165551. [CrossRef] [Google Scholar]
  6. K. Kumar et al., “From Homogeneity to Heterogeneity: Designing Functionally Graded Materials for Advanced Engineering Applications,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01198. [Google Scholar]
  7. M. Z. ul Haq et al., “Waste Upcycling in Construction: Geopolymer Bricks at the Vanguard of Polymer Waste Renaissance,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01205. [Google Scholar]
  8. M. Z. ul Haq, H. Sood, and R. Kumar, “SEM-Assisted Mechanistic Study: pH-Driven Compressive Strength and Setting Time Behavior in Geopolymer Concrete,” 2023. [Google Scholar]
  9. V. Sharma and S. Singh, “Modeling for the use of waste materials (Bottom ash and fly ash) in soil stabilization,” Mater Today Proc, vol. 33, pp. 1610–1614, Jan. 2020, doi: 10.1016/J.MATPR.2020.05.569. [CrossRef] [Google Scholar]
  10. Md. Z. U. Haq, H. Sood, R. Kumar, and I. Merta, “Taguchi-optimized triple-aluminosilicate geopolymer bricks with recycled sand: A sustainable construction solution,” Case Studies in Construction Materials, vol. 20, p. e02780, 2024, doi: https://doi.org/10.1016/j.cscm.2023.e02780. [Google Scholar]
  11. A. Winkler, W. Wang, A. Norouzi, D. Gordon, C. R. Koch, and J. Andert, “Integrating Recurrent Neural Networks into Model Predictive Control for Thermal Torque Derating of Electric Machines,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 8254–8259, 2023, doi: 10.1016/J.IFACOL.2023.10.1010. [CrossRef] [Google Scholar]
  12. J. Hong, Z. Wang, W. Chen, L. Y. Wang, and C. Qu, “Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles,” J Energy Storage, vol. 30, Aug. 2020, doi: 10.1016/j.est.2020.101459. [CrossRef] [Google Scholar]
  13. X. Lin, J. Zhang, and L. Su, “A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction,” J Energy Storage, vol. 52, Aug. 2022, doi: 10.1016/j.est.2022.105055. [Google Scholar]
  14. A. Mousaei, M. Gheisarnejad, and M. H. Khooban, “Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review,” J Energy Storage, vol. 73, Dec. 2023, doi: 10.1016/j.est.2023.108860. [CrossRef] [Google Scholar]
  15. Y. Wu, Y. Zhang, G. Li, J. Shen, Z. Chen, and Y. Liu, “A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks,” Energy, vol. 208, Oct. 2020, doi: 10.1016/j.energy.2020.118366. [Google Scholar]
  16. M. Subbarao, K. Dasari, S. S. Duvvuri, K. R. K. V. Prasad, B. K. Narendra, and V. B. Murali Krishna, “Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles,” Measurement: Sensors, vol. 31, p. 101001, Feb. 2024, doi: 10.1016/J.MEASEN.2023.101001. [CrossRef] [Google Scholar]
  17. Y. Gao, S. Yang, Y. Chen, W. Li, J. Yang, and F. Yi, “Multi-physical cooperative control of plug-in hybrid electric vehicles via cyber hierarchy and interactional network,” Commun Nonlinear Sci Numer Simul, vol. 120, Jun. 2023, doi: 10.1016/j.cnsns.2023.107158. [Google Scholar]
  18. A. Mousa, “Extended-deep Q-network: A functional reinforcement learning-based energy management strategy for plug-in hybrid electric vehicles,” Engineering Science and Technology, an International Journal, vol. 43, Jul. 2023, doi: 10.1016/j.jestch.2023.101434. [CrossRef] [Google Scholar]
  19. “Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks – Search | ScienceDirect.com.” Accessed: Jan. 05, 2024. [Online]. Available: https://www.sciencedirect.com/search?qs=Real-time%20Adaptive%20Control%20of%20Electric%20Vehicle%20Drives%20using%20Artificial%20Neural%20Networks [Google Scholar]
  20. W. Tang, Y. Wang, X. Jiao, and L. Ren, “Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios,” Energy, vol. 265, Feb. 2023, doi: 10.1016/j.energy.2022.126264. [CrossRef] [Google Scholar]
  21. H. Jondhle, A. B. Nandgaonkar, S. Nalbalwar, and S. Jondhle, “An artificial intelligence and improved optimization-based energy management system of battery-fuel cell-ultracapacitor in hybrid electric vehicles,” J Energy Storage, vol. 74, Dec. 2023, doi: 10.1016/j.est.2023.109079. [CrossRef] [Google Scholar]
  22. A. B. Çolak, “A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning approach,” J Energy Storage, vol. 70, Oct. 2023, doi: 10.1016/j.est.2023.108101. [Google Scholar]
  23. M. Kurucan, M. Özbaltan, Z. Yetgin, and A. Alkaya, “Applications of artificial neural network based battery management systems: A literature review,” Renewable and Sustainable Energy Reviews, vol. 192, p. 114262, Mar. 2024, doi: 10.1016/J.RSER.2023.114262. [CrossRef] [Google Scholar]
  24. J. Xia, F. Wang, and X. Xu, “A predictive energy management strategy for multi-mode plug-in hybrid electric vehicle based on long short-term memory neural network,” IFAC-PapersOnLine, vol. 54, no. 10, pp. 132–137, 2021, doi: 10.1016/j.ifacol.2021.10.153. [CrossRef] [Google Scholar]
  25. R. Vignesh, B. Ashok, M. Senthil Kumar, D. Szpica, A. Harikrishnan, and H. Josh, “Adaptive neuro fuzzy inference system-based energy management controller for optimal battery charge sustaining in biofuel powered non-plugin hybrid electric vehicle,” Sustainable Energy Technologies and Assessments, vol. 59, Oct. 2023, doi: 10.1016/j.seta.2023.103379. [CrossRef] [Google Scholar]
  26. N. Yang et al., “Real-time adaptive energy management for off-road hybrid electric vehicles based on decision-time planning,” Energy, vol. 282, Nov. 2023, doi: 10.1016/j.energy.2023.128832. [Google Scholar]
  27. Z. Chen, Y. Liu, Y. Zhang, Z. Lei, Z. Chen, and G. Li, “A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles,” Energy, vol. 243, Mar. 2022, doi: 10.1016/j.energy.2021.122727. [Google Scholar]
  28. Rimsha et al., “State of charge estimation and error analysis of lithium-ion batteries for electric vehicles using Kalman filter and deep neural network,” J Energy Storage, vol. 72, Nov. 2023, doi: 10.1016/j.est.2023.108039. [CrossRef] [Google Scholar]
  29. T. Zhu, R. G. A. Wills, R. Lot, H. Ruan, and Z. Jiang, “Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting,” Appl Energy, vol. 292, Jun. 2021, doi: 10.1016/j.apenergy.2021.116932. [Google Scholar]
  30. B. P. Adedeji, “A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption,” Green Energy and Intelligent Transportation, vol. 2, no. 2, Apr. 2023, doi: 10.1016/j.geits.2023.100070. [CrossRef] [Google Scholar]
  31. Y. Zhang, D. Zhao, L. He, Y. Zhang, and J. Huang, “Research on prediction model of electric vehicle thermal management system based on particle swarm optimization– Back propagation neural network,” Thermal Science and Engineering Progress, vol. 47, p. 102281, Jan. 2024, doi: 10.1016/j.tsep.2023.102281. [CrossRef] [Google Scholar]
  32. R. Liu, C. Wang, A. Tang, Y. Zhang, and Q. Yu, “A twin delayed deep deterministic policy gradient-based energy management strategy for a battery-ultracapacitor electric vehicle considering driving condition recognition with learning vector quantization neural network,” J Energy Storage, vol. 71, Nov. 2023, doi: 10.1016/j.est.2023.108147. [Google Scholar]
  33. F. Millo, L. Rolando, L. Tresca, and L. Pulvirenti, “Development of a neural network-based energy management system for a plug-in hybrid electric vehicle,” Transportation Engineering, vol. 11, Mar. 2023, doi: 10.1016/j.treng.2022.100156. [CrossRef] [Google Scholar]
  34. A. Manoharan, K. M. Begam, V. R. Aparow, and D. Sooriamoorthy, “Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review,” J Energy Storage, vol. 55, Nov. 2022, doi: 10.1016/j.est.2022.105384. [CrossRef] [Google Scholar]
  35. W. Li et al., “Regenerative braking control strategy for pure electric vehicles based on fuzzy neural network,” Ain Shams Engineering Journal, Feb. 2023, doi: 10.1016/j.asej.2023.102430. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.