306
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Optimizing energy management strategies for hybrid electric ships based on condition identification and model predictive control

, , , &
Pages 1763-1775 | Received 16 Nov 2022, Accepted 20 Mar 2023, Published online: 29 Mar 2023

References

  • Balsamo, F., C. Capasso, D. Lauria, and O. Veneri. 2020. Optimal design and energy management of hybrid storage systems for marine propulsion applications. Applied Energy 278 (Nov.15):.115629.1–11. doi:10.1016/j.apenergy.2020.115629.
  • Bassam, A. M., A. B. Phillips, S. R. Turnock, and P. A. Wilson. 2017. Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship. International Journal of Hydrogen Energy 42 (1):623–35. doi:10.1016/j.ijhydene.2016.08.209.
  • Besikci, E. B., T. Kececi, O. Arslan, and O. Turan. 2016. An application of fuzzy-AHP to ship operational energy efficiency measures. Ocean Engineering 121 (15):392–402. doi:10.1016/j.oceaneng.2016.05.031.
  • Byun, H., and S. W. Lee. 2003. A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence 17 (3):459–86. doi:10.1142/S0218001403002460.
  • Cao, X. F., S. Gao, L. C. Chen, and Y. Wang. 2019. Ship recognition method combined with image segmentation and deep learning feature extraction in video surveillance. Multimedia Tools and Applications 79 (13–14):9177–92. doi:10.1007/s11042-018-7138-3.
  • Ericsson, E. 2001. Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D 6 (5):325–45. doi:10.1016/S1361-9209(01)00003-7.
  • Gao, D. J., Y. Jiang, and N. Zhao. 2020. A novel load prediction method for hybrid electric ship based on working condition classification. Transactions of the Institute of Measurement and Control 44 (1):5–14. doi:10.1177/0142331220923767.
  • Guo, J. Q., H. W. He, J. K. Peng, and N. Zhou. 2019. A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles. Energy 175 (May 15):378–92. doi:10.1016/j.energy.2019.03.083.
  • Han, J. G., J. F. Charpentier, and T. H. Tang. 2014. An energy management system of a fuel cell/Battery Hybrid Boat. Energies 7 (5):56–96. doi:10.3390/en7052799.
  • Hou, Z. R., J. H. Guo, J. M. Xing, C. Guo, and Y. Zhang. 2021. Machine learning and whale optimization algorithm based design of energy management strategy for plug-in hybrid electric vehicle. IET Intelligent Transport Systems 15 (8):1076–91. doi:10.1049/itr2.12084.
  • Hou, J., Z. Y. Song, H. Hofmann, and J. Sun. 2019. Adaptive model predictive control for hybrid energy storage energy management in all-electric ship microgrids. Energy Conversion and Management 198 (10):198–212. doi:10.1016/j.enconman.2019.111929.
  • Luo, Y. G., and S. W. Z. Chen, T., and K. Q. Li, K. Li. 2015. “Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort.” IEEE Transactions on Intelligent Transportation Systems 16(4): 2303–08. doi: 10.1109/TITS.2014.2387356.
  • Ma, C., Y. Kun, L. D. Miao, M. Q. Chen, and S. Gao. 2019. Development of driving condition classification based adaptive optimal control strategy for PHEV. International Journal of Electric and Hybrid Vehicles 11 (3):235–54. doi:10.1504/IJEHV.2019.101299.
  • Ovrum, E., and T. F. Bergh. 2015. Modelling lithium-ion battery hybrid ship crane operation. Applied Energy 152:162–72. doi:10.1016/j.apenergy.2015.01.066.
  • Planakis, N., G. Papalambrou, and N. Kyrtatos. 2021. Predictive power-split system of hybrid ship propulsion for energy management and emissions reduction. Control Engineering Practice 111:111104795-1-104795–13. doi:10.1016/j.conengprac.2021.104795.
  • Song, K., F. Q. Li, X. Hu, L. He, W. X. Niu, S. H. Lu, and T. Zhang. 2018. Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern recognition using learning vector quantization neural network algorithm. Journal of Power Sources 389:230–39. doi:10.1016/j.jpowsour.2018.04.024.
  • Tang, R. L., X. Li, and J. G. Lai. 2018. A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization. Applied Energy 228:254–64. doi:10.1016/j.apenergy.2018.06.092.
  • Torreglosa, J. P., P. García, L. M. Fernández, and F. Jurado. 2014. Hierarchical energy management system for stand-alone hybrid system based on generation costs and cascade control. Energy Conversion and Management 77 (2):514–26. doi:10.1016/j.enconman.2013.10.031.
  • Xiao, N. Q., X. Xu, and B. J. Chen. 2020. Research on simulation and experiment of ship complex diesel-electric hybrid propulsion system. Journal of Ship Research 64 (2):171–84. doi:10.5957/jsr.2020.64.2.171.
  • Xie, S. S., H. W. He, and J. K. Peng. 2017. An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses. Applied Energy 196 (Jun.15):279–88. doi:10.1016/j.apenergy.2016.12.112.
  • Yan, X. P. 2010. Progress review of new energy application in ship. Ship & Ocean Engineering 39 (6):111–15. doi:10.3963/j.issn.1671-7953.2010.06.031.
  • Yan, F. J., J. M. Wang, and K. S. Huang. 2012. Hybrid electric vehicle model predictive control torque-split strategy incorporating engine transient characteristics. IEEE Trans Vehicular Technology 61 (6):2458–67. doi:10.1109/TVT.2012.2197767.
  • Yuan, Y. P., J. X. Wang, X. P. Yan, B. Shen, and T. Long. 2020. A review of multi-energy hybrid power system for ships. Renewable and Sustainable Energy Reviews 132:1–20. doi:10.1016/j.rser.2020.110081.
  • Zhang, Y. J., L. Chu, Z. C. Fu, N. Xu, C. Guo, X. Z. Zhang, Z. H. Chen, and P. Wang. 2017. Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction. Mechatronics 46:177–92. doi:10.1016/j.mechatronics.2017.08.008.
  • Zhu, Y. C., and Y. Zheng. 2020. Traffic identification and traffic analysis based on support vector machine. Neural Computing & Applications 32 (7):1903–11. doi:10.1007/s00521-019-04493-2.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.