PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

The limited driving range of BEVs is the main challenge in developing zero-emission Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This limitation necessitates an increase in battery energy while balancing the power supply and consumption requirements for the vehicle’s motor and auxiliaries, such as the Heating, Ventilation, and Air Conditioning (HVAC) system. This research proposes a solution to achieve more efficient control of HVAC consumption by integrating fuzzy logic techniques with brute-force algorithms to optimize the Energy Management System (EMS) in BEVs. The model was based on actual parameters, implemented using MATLAB-Simulink and ADVISOR software, and configured using a backward-facing design incorporating the technical specifications of a Malaysian electric car, the PROTON IRIZ. An optimal solution was proposed based on the Satisfaction Ratio (SR) and State of Charge (SoC) metrics to achieve the best system optimization. The results demonstrate that the optimized fuzzy EMS improved power consumption by 23.2% to 26.6% compared to a basic fuzzy EMS. The proposed solution significantly improves the driving range of BEVs.

• Dou, H., Zhang, Y., & Fan, L. (2021). Design of optimized energy management strategy for all-wheel-drive electric vehicles. Applied Sciences, 11(17), 1-14. https://doi.org/10.3390/app11178218

• Eberle, U., & von Helmolt, R. (2010). Sustainable transportation based on electric vehicle concepts: A brief overview. Energy & Environmental Science, 3(6), 689-699. https://doi.org/10.1039/C001674H

• Giakoumis, E. G. (2017). Driving and Engine Cycles. Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-49034-2

• Górriz, J. M., Ramírez, J., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Suckling, J., Leming, M., Zhang, Y. D., Álvarez-Sánchez, J. R., Bologna, G., Bonomini, P., Casado, F. E., Charte, D., Charte, F., Contreras, R., Cuesta-Infante, A., Duro, R. J., Fernández-Caballero, A., Fernández-Jover, E., … & Ferrández, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237-270. https://doi.org/10.1016/j.neucom.2020.05.078

• Han, S., Zhang, F., & Xi, J. (2018). A real-time energy management strategy based on energy prediction for parallel hybrid electric vehicles. IEEE Access, 6, 70313-70323. https://doi.org/10.1109/ACCESS.2018.2880751

• Hassanzadeh, M., & Rahmani, Z. (2022). A predictive controller for real-time energy management of plug-in hybrid electric vehicles. Energy, 249, Article 123633. https://doi.org/10.1016/j.energy.2022.123663

• Hu, J., Niu, X., Jiang, X., & Zu, G. (2019). Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle. International Journal of Energy Research, 43(8), 3346-3364. https://doi.org/10.1002/er.4474

• Hu, X., Zheng, Y., Howey, D. A., Perez, H., Foley, A., & Pecht, M. (2020). Battery warm-up methodologies at subzero temperatures for automotive applications: Recent advances and perspectives. Progress in Energy and Combustion Science, 77, Article 100806. https://doi.org/10.1016/j.pecs.2019.100806

• Hussain, S., Ali, M. U., Park, G. S., Nengroo, S. H., Khan, M. A., & Kim, H. J. (2019). A real-time bi-adaptive controller-based energy management system for battery-supercapacitor hybrid electric vehicles. Energies, 12(4662), 1-24. https://doi.org/10.3390/en12244662

• Masjosthusmann, C., Köhler, U., Decius, N., & Büker, U. (2012). A vehicle energy management system for a battery electric vehicle. In 2012 IEEE Vehicle Power and Propulsion Conference (pp. 339-344). IEEE Publishing. https://doi.org/10.1109/VPPC.2012.6422676

• Mohd, T. A. T. (2020). Development of Optimal Energy Management Topology for Battery Electric Vehicle with Load Segmentation [Doctoral dissertation]. Universiti Putra Malaysia, Malaysia. http://psasir.upm.edu.my/id/eprint/92800/1/FK%20%202020%20104%20IR.pdf

• Pan, C., Gu, X., Chen, L., Yi, F., & Zhou, J. (2021). Fuzzy optimal energy management for battery electric vehicles concerning equivalent speed. International Transactions on Electrical Energy Systems, 31(1), 1-15. https://doi.org/10.1002/2050-7038.12527

• Pham, C., & Månsson, D. (2018). Optimal energy storage sizing using equivalent circuit modelling for prosumer applications (Part II). Journal of Energy Storage, 18, 1-15. https://doi.org/10.1016/j.est.2018.04.015

• Tammi, K., Minav, T., & Kortelainen, J. (2018). Thirty years of electro-hybrid powertrain simulation. IEEE Access, 6, 35250-35259. https://doi.org/10.1109/ACCESS.2018.2850916

• Temiz, A. (2015). Assessment of Impacts of Electric Vehicles on Low Voltage Distribution Networks in Turkey [Master dissertation]. Middle East Technical University, Turkey. http://etd.lib.metu.edu.tr/upload/12619200/index.pdf

• Valentina, R., Viehl, A., Bringmann, O., & Rosenstiel, W. (2014). HVAC system modeling for range prediction of electric vehicles. In 2014 IEEE Intelligent Vehicles Symposium Proceedings (pp. 1145-1150). IEEE Publishing. https://doi.org/10.1109/IVS.2014.6856500

• Zhang, F., Xi, J., & Langari, R. (2017). Real-time energy management strategy based on velocity forecasts using V2V and V2I communications. IEEE Transactions on Intelligent Transportation Systems, 18(2), 416-430. https://doi.org/10.1109/TITS.2016.2580318