Energy Management System for Autonomous Vehicle

Resumen: The present work focuses on the energy management for autonomous vehicle with generation forecasting. The objective is to find the optimal handling of the storage system. The energy come from renewable sources and storage devices. To this aim, a unified model of the hybrid renewable energy system , operated in autonomous mode, has been developed. The aim of this paper consists of optimal handling of the storage system within the hybrid renewable energy system considering known predictions of solar radiation and wind speed for a period, T. The optimization model is solved using a hybrid genetic algorithm with interior point (GA-IP) provided by the optimization toolbox MATLAB®, The case study considers the hybrid renewable energy system with the photovoltaic panels, the wind turbines and the batteries for energy storage. In this test, the power supplied by renewable sources is enough to feed the motors and charge the storage system.

¿Cómo citar?

Luis O. Polanco, Víctor M. Ramírez & Diego Langarica. Energy Management System for Autonomous Vehicle. Memorias del Congreso Nacional de Control Automático, pp. 176-181, 2019.

Palabras clave

Sistemas Electrónicos de Potencia, Sistemas Estocásticos, Redes Neuronales

• Bellini A., Bifaretti S., Iacovone V. and Cornaro C., (2009), “Simplified model of a photovoltaic module," Applied Electronics Pilsen, 2009, pp. 47-51.
• Bidram A. and Davoudi A., (2012), “Hierarchical Structure of Microgrids Control System”, IEEE Transactions on Smart Grid, volume (3), no. 4, December. 2012, pp. 1963-1976.
• Gill S., Kockar I. and Ault G. W., (2014) “Dynamic Optimal Power Flow for Active Distribution Networks", IEEE Transactions on Power Systems, volume (29), no. 1, Jan. 2014, pp. 121-131.
• Katiraei F., Iravani R., Hatziargyriou N. and Dimeas A., (2008), “Microgrids management,", IEEE Power and Energy Magazine, vol. (6), no. 3, May-June 2008, pp. 54-65.
• Mahyiddin et al S. H., (2016), “Fuzzy logic energy management system of series hybrid electric vehicle," 4th IET Clean Energy and Technology Conference (CEAT 2016), Kuala Lumpur, 2016, pp. 1-6.
• Olivares D. E., Cañizares C. A. and Kazerani M., (2014) “A Centralized Energy Management System for Isolated Microgrids", IEEE Transactions on Smart Grid, volume (5), no. 4, July 2014 , pp. 1864-1875.
• The MathWorks Inc., (2016a), Matlab Optimization Toolbox. Users Guide R2016b. Available at: http://www.mathworks.com/help/pdf_doc/optim/optimtb.pdf The MathWorks Inc., (2016b), Global Optimization Toolbox, Users Guide R2016b, Available at: http://www.mathworks.com/help/pdf_doc/gads/gads_tb.pdf
• Polanco L., Carreño, C., Pizano, A., López J, Pérez M, Álvarez Hervás, J.D. Optimal Energy Management within a Microgrid: A Comparative Study. Energies 2018, 11, 2167.
• Silfab Solar Inc, Data sheet module Silfab SLAM 300, (english), (2015), Available at: http://www.silfabsolar.com/wpcontent/uploads/2019/02/Silfab-SLA-M-300-SF-0G20190131-K-Final.pdf.
• Wang L., Yeh T. H., Lee W. J. and Z. Chen, (2009) “Benefit Evaluation of Wind Turbine Generators in Wind Farms Using Capacity-Factor Analysis and Economic-Cost Methods", IEEE Transactions on Power Systems, volume (24), no. 2, pp. 692-704, May 2009.
• Zúñiga P., (2006), “Analysis and control of a series compensator”, Ph.D. dissertation, Dept. Electrical Power Systems, CINVESTAV Gdl., Guadalajara, México 2006. Available at http://www.gdl.cinvestav.mx/jramirez/doctos/doctorado/Tesi s_pavel2006.pdf