Keywords
hybrid vehicles
fuzzy control
rule-based control
How to Cite
Abstract
This paper presents the design and simulation of fuzzy control and rule-based control for energy management in a parallel mild hybrid electric vehicle. The management system must minimize fuel consumption while ensuring load maintenance and ensuring component limitations. Equivalent fuel consumption is used to evaluate control performance, which penalizes the battery's use of electrical energy and fuel consumption but does not reward the storage of electrical energy in the battery. The presented model was tested in two different driving cycles, Europe: NEDC and USA: FTP-75 with an efficiency of 17.6% and 18.3% respectively. However, this result could be improved with more in-depth testing and error estimation, as well as more advanced simulations. In addition, more rules can be added to the proposed model, giving this approach scalable functionality.
References
[2] M. Zand, M. A. Nasab, A. Hatami, M. Kargar y H. R. Chamorro, «Using Adaptive Fuzzy Logic for Intelligent Energy Management in Hybrid Vehicles,» de 2020 28th Iranian Conference on Electrical Engineering, Tabriz, Irán, 2020.
[3] M. Farrag, C. S. Lai, M. Darwish y T. Gareth, «Improving the Efficiency of Electric Vehicles: Advancements in,» Vehicles, vol. 6, nº 2024, pp. 1089-1113, 2024.
[4] A. Recalde, R. Cajo, W. Velasquez y M. S. Alvarez-Alvarado, «Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review,» Energies, vol. 17, nº 13, p. 3059, 2024.
[5] C. Armenta-Déu, «Battery Management for Improved Performance in Hybrid Electric Vehicles,» Vehicles, vol. 6, nº 2, pp. 949-966, 2024.
[6] T. Aljohani, «Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration,» Energies, vol. 12, nº 17, p. 2949, 2024.
[7] L. Dalhoumi, M. Chtourou y M. Djemel, «Model based predictive control for linear interconnected systems,» de IEEE SSD International Multi-Conference on Systems, Signals and Devices, Leipzig, Germany, 2016.
[8] S. K. Chada, D. Görges y A. Ebert, «Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios,» IFAC-PapersOnLine, vol. 56, nº 2, pp. Pages 1107-1114, 2023.
[9] N. Mort, S. Cater y D. Langbridge, «Experiences with rule-based control algorithms in a teaching laboratory and a diesel engine test cell,» de IEE Colloquium on Exploiting the Knowledge Base: Applications of Rule Based Control, London, UK, 1989.
[10] P. Zhang, Advanced Industrial Control Technology, Oxford, UK: Springer, 2010.
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