Fuzzy control of dual storage system of an electric drive vehicle considering battery degradation

Kemal Keskin; Burak Urazel

doi:10.11121/ijocta.01.2021.00848

Authors

Kemal Keskin Eskisehir Osmangazi University http://orcid.org/0000-0002-3969-2396
Burak Urazel Eskisehir Osmangazi University http://orcid.org/0000-0002-3221-9854

DOI:

https://doi.org/10.11121/ijocta.01.2021.00848

Keywords:

Fuzzy control, electric vehicle, dual storage system, energy management

Abstract

In this manuscript, fuzzy logic energy management strategy for dual storage system inclu\-ding supercapacitors and battery is proposed in order to prolong battery lifespan and enhance the range of electric drive vehicle (EDV). First an EDV model and three drive cycles (NEDC, UDDS, and NREL) are established in Matlab/Simulink. Then a fuzzy inference system is designed considering three inputs: power demand, state of charge (SOC) of battery and SOC of supercapacitors. An output, which refers to split ratio between supercapacitors and battery power, is determined. Fuzzy rules are constituted in order to decrease not only high level battery current but also number of charge/discharge cycle of battery which are the main factors of battery deterioration. For a performance verification of the proposed method, three drive cycles with different characteristics are considered. Obtained results are compared to two other strategies; one of them is battery only system and the other one is dual storage system managed by logic threshold method. It is shown that the proposed method delivers better and robust performance to prolong battery lifespan.

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Author Biographies

Kemal Keskin, Eskisehir Osmangazi University

Kemal Keskin received his B.Sc. degree from Dumlupinar University, Kutahya, Turkey, in 2006. He received his M.Sc degree from Northeastern University, Boston, USA, in 2009 and he received Ph.D. degree from Eskisehir Osmangazi University, Eskisehir, Turkey, in 2016. He is currently working as assistant professor at Electrical and Electronics Engineering Department of Eskişehir Osmangazi University, Eskisehir, Turkey. His research interests include optimization, control systems and artificial intelligence.

Burak Urazel, Eskisehir Osmangazi University

Burak Urazel received his B.Sc. degree from Anadolu University, Eskişehir, Turkey, in 2008, his M.Sc and Ph.D. degrees from Eskişehir Osmangazi University, Eskişehir, Turkey, in 2011 and in 2017. He is currently working as an assistant professor at the Electrical and Electronics Engineering Department of Eskişehir Osmangazi University, Eskisehir, Turkey. His research interests include optimization methods and their application in power systems.

References

Environmental Protection Agency(EPA) (2019). Invertory of U.S. Greenhouse Gas Emissions and Sinks, EPA 430-R-19-001.

Chan, C. (2002). The state of the art of electric and hybrid vehicles. Proceedings of the IEEE, 90(2), 247-275.

Burke, A. (2007). Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles. Proceedings of the IEEE, 95(4), 806-820.

Landi, M. Gross, G. (2014). Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications. IEEE Transactions on Instrumentation and Measurement, 63(5), 1224-1234.

Choi, M., Lee, J., Seo, S. (2014). Real-Time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles. IEEE Transactions on Vehicular Technology, 63(8), 3600-3611.

Auer, J., Sartorelli, G., Miller, J. (2006). A Gatekeeper energy management strategy for ECVT hybrid vehicle propulsion utilising ultracapacitors. IET - Hybrid Vehicle Conference. 79-90.

Kouchachvili, L., Yaici W., Entchev, E. (2018). Hybrid battery/supercapacitor energy storage system for the electric vehicles. Journal of Power Sources. 374, 237-248.

Khaligh A., Li, Z. (2010). Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art. IEEE Transactions on Vehicular Technology. 59(6), 2806-2814.

Zhang, S., Xiong, R. Cao, J. (2016). Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system. Applied Energy. 179, 316-328.

Fadil, H., Giri, F., Guerrero, J., Tahri, A. (2014). Modeling and Nonlinear Control of a Fuel Cell/Supercapacitor Hybrid Energy Storage System for Electric Vehicles. IEEE Transactions on Vehicular Technology. 63(7), 3011-3018.

Song, Z., Hou, J., Hofmann, H., Li, J., Ouyang, M. (2017). Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles. Energy. 122, 601-612.

Salazar M., Balerna, C., Elbert, P., Grando, F., Onder, C. (2017). Real-Time Control Algorithms for a Hybrid Electric Race Car Using a Two-Level Model Predictive Control Scheme. IEEE Transactions on Vehicular Technology. 66(12), 10911-10922.

Song, Z., Li, J., Hou, J., Hofmann, H., Ouyang, M., Du, J. (2018). The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study. Energy. 154, 433-441.

Li, Q., Chen, W., Liu, Z., Li, M., Ma, L. (2015). Development of energy management system based on a power sharing strategy for a fuel cell-battery-supercapacitor hybrid tramway. Journal of Power Sources. 279, 267-280.

Xiong, R., Cao, J., Yu, Q. (2018). Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Applied Energy. 211, 538-548.

Moreno, J., Ortuzar, M. E., Dixon, J. W. (2006). Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks. IEEE Transactions on Industrial Electronics. 53(2), 614–623.

Shen, J., Khaligh, A. (2015). A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System. IEEE Transactions on Transportation Electrification. 1(3), 223–231.

Islam, M., Yang, F., Ekanayek, C., Amin, M. (2018). Grid power fluctuation reduction by fuzzy control based energy management system in residential microgrids. International Transactions on Electrical Energy Systems. 29(3), 2758.

Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., Guinjoan, F. (2018). Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids. IEEE Transactions on Smart Grid. 9(2), 530–543.

Prathyush, M., Jasmin, E. A. (2018). Fuzzy Logic Based Energy Management System Design for AC Microgrid, in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

Xiong, W., Zhang, Y., Yin, C. (2009). Configuration design, energy management and experimental validation of a novel series-parallel hybrid electric transit bus. Journal of Zhejiang University-SCIENCE A. 10(9), 1269-1276.

Madani, O., Bhattacharjee, A., Das, T. (2016). Decentralized Power Management in a Hybrid Fuel Cell Ultracapacitor System. IEEE Transactions on Control Systems Technology. 24(3), 765-778.

Mitschke, M., Dynamik der Kraftzeuge (1995). Band A- Antrieb und Bremsung. 3rd ed. Springer Publication.

Bhatt, A. (2016). Planning and application of Electric Vehicle with MATLAB®/Simulink®. In 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

Jun, R., Kai, W., Liwei, L. (2017). Characteristics analysis of ultracapacitor-battery hybrid energy storage system, in 2017 Chinese Automation Congress (CAC).

Zadeh, L. (1988). Fuzzy logic. Computer. 21(4), 83-93.

Yang, G., Li, J., Fu, Z., Fang, L., (2018). Optimization of logic threshold control strategy for electric vehicles with hybrid energy storage system by pseudo-spectral method. Energy Procedia. 152, 508-513.

Yuan, Y., Zhang, J., Li, Y. & Li, C. (2018). A Novel Regenerative Electrohydraulic Brake System: Development and Hardware-in-Loop Tests. IEEE Transactions on Vehicular Technology. 67(12), 11440-11452.