Gestión energética de vehículos híbridos mediante métodos de control robusto moderno
DOI:
https://doi.org/10.56183/iberotecs.v3i1.589Palavras-chave:
Control predictivo económico, vehículos híbridos, pilas de combustible, baterías, supercondensadoresResumo
In this paper, economic predictive control is proposed as a technique for optimal energy management of a hybrid vehicle. The model used for the control is described and from it an economic predictive controller is designed. Finally, the best adjustment of the controller weights that achieves the greatest reduction in the consumption of the fuel cell with the help of batteries and supercapacitors is studied. To do this, the maximum and minimum consumption points of the fuel cell are determined, and the multi-objective control problem is characterized by determining the Pareto curves. The article concludes with a discussion of the results and future work.
Referências
Agarwal, V., Dev, M. (2013). Introduction to hybrid electric vehicles: State of art. In Engineering and Systems (SCES), 2013 Students Conference on (pp. 1-6). IEEE.
Agarwal, V., Saxena, R. (2014). An Introduction to Fuel Cell Electric Vehicles: State of Art. MIT International Journal of Electrical & Instrumentation Engineering, 4(1), 35-38.
Azib, T., Hemsas, K. E., & Larouci, C. (2014). Energy Management and Control Strategy of Hybrid Energy Storage System for Fuel Cell Power Sources. International Review on Modelling and Simulations (IREMOS), 7(6), 935-944.
Carter, R., Cruden, A., & Hall, P. J. (2012). Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle. Vehicular Technology, IEEE Transactions on, 61(4), 1526-1533.
Ehsani, M., Gao, Y., & Emadi, A. (2009). Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design. CRC press.
Feroldi, D., Serra, M., & Riera, J. (2009). Energy management strategies based on efficiency map for fuel cell hybrid vehicles. Journal of Power Sources, 190(2), 387-401.
Haitao, Y., Yulan, Z., Zunnian, L., & Kui, H. (2013). LQR-Based Power Train Control Method Design for Fuel Cell Hybrid Vehicle. Mathematical Problems in Engineering, 2013.
International energy Agency, “Key World Energy Statics” http://www.iea.org 2015.
Khurana, H., Hadley, M., Lu, N., & Frincke, D. A. (2010). Smart-grid security issues. IEEE Security & Privacy, (1), 81-85.
Sciarretta, A., De Nunzio, G., & Ojeda, L. L. (2015). Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem. Control Systems, IEEE, 35(5), 71-90.
Serrao, L., Sciarretta, A., Grondin, O., Chasse, A., Creff, Y., Di Domenico, D., & Thibault, L. (2013). Open issues in supervisory control of hybrid electric vehicles: A unified approach using optimal control methods. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles, 68(1), 23-33.
Toro, R. (2010). Smart tuning of predictive controllers for drinking water networked systems (Doctoral dissertation, Master’s thesis, Universitat Politecnica de Catalunya).
Xu, N., Zhang, Y., Fu, Z., Zhao, D., Chu, L., & Zhou, F. (2015, April). Investigation of Topologies and Control Strategies of Fuel Cell Vehicles. In2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics. Atlantis Press
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2023 Joseph Eli Izquierdo Obando , Aldo Patricio Mora Olivero , Cindy Johanna Choez Calderón, David Leonardo Rodríguez Portes
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.