Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy

Öz

In this study, a model of a pre-transmission parallel hybrid vehicle was created in MATLAB/Simulink environment. A torque control strategy has been developed for the hybrid vehicle on the created model. The hybrid vehicle's torque control is provided by fuzzy logic and conventional rule-based control strategies. The control strategies of the hybrid vehicle have been developed based on four input parameters: accelerator and brake pedal position, battery state of charge and the operating mode of the electric motor. The regenerative braking system of the pre-transmission parallel hybrid vehicle has been provided with a fuzzy logic control strategy in both vehicle structures. In the fuzzy logic torque split controller, a control algorithm has been created in which the safety, performance and fuel consumption values of the vehicle meet at the optimum point. Under different driving cycle conditions, the effects of control strategies on engine operating points have been examined and average fuel consumption values have been obtained. In the control of the parallel hybrid vehicle with fuzzy logic method, it has been obtained that it provides 12.87%, 6.62%, 2.27% and 6.23% fuel savings under FTP-75, NEDC, EUDC and ECE-15 driving cycle conditions, respectively. It has been observed that the vehicle cannot provide sufficient performance in US06 driving cycle conditions with the use of conventional rule-based control strategy.

Anahtar Kelimeler

• Fuzzy logic
• energy saving
• rule-based control strategy
• modeling
• parallel hybrid

Ön İletimli Paralel Hibrit Aracın Bulanık Mantık Yöntemi ile Kontrolünün ve Optimizasyonunun Yapılması ve Geleneksel Kural Tabanlı Kontrol Stratejisi ile Karşılaştırılması

Öz

Bu çalışmada, MATLAB/Simulink ortamında çn iletimli paralel hibrit araç modeli oluşturulmuştur. Oluşturulan model üzerinde hibrit araç için tork kontrol stratejisi geliştirilmiştir. Hibrit aracın tork kontrolü, bulanık mantık ve geleneksel kural tabanlı kontrol stratejileri ile sağlanmıştır. Hibrit aracın kontrol stratejileri, gaz ve fren pedalı konumu, batarya şarj durumu ve elektrik motorunun çalışma modu olmak üzere dört girdi parametresine dayalı olarak geliştirilmiştir. Ön iletimli paralel hibrit aracın rejeneratif fren sistemi, her iki araç yapısında da bulanık mantık kontrol stratejisi ile sağlanmıştır. Bulanık mantık tork dağıtıcı denetleyicide aracın güvenlik, performans ve yakıt tüketim değerlerinin optimum noktada buluştuğu bir kontrol algoritması oluşturulmuştur. Farklı sürüş çevrimi koşulları altında, kontrol stratejilerinin motor çalışma noktaları üzerindeki etkileri incelenmiş ve ortalama yakıt tüketim değerleri elde edilmiştir. Bulanık mantık yöntemi ile paralel hibrit aracın kontrolünde, FTP-75, NEDC, EUDC ve ECE-15 sürüş çevrimi koşullarında sırasıyla %12.87, %6.62, %2.27 ve %6.23 yakıt tasarrufu sağlandığı sonucu elde edilmiştir. Konvansiyonel kural tabanlı kontrol stratejisinin kullanılması ile aracın US06 sürüş çevrimi koşullarında yeterli performansı sağlayamadığı gözlemlenmiştir.

Anahtar Kelimeler

• Bulanık mantık
• enerji tasarrufu
• kural tabanlı kontrol stratejisi
• modelleme
• paralel hibrit

Kaynakça

1. [1] Çiçek, A., Erdinç, O., “Charge management of electric vehicle parking lot with PV-battery hybrid system”, European Journal of Science and Technology, 15, 466-474, (2019).
2. [2] Erdem, Y., Taci, S., “Effect of regenerative braking and power analysis in electric vehicles. Journal of Current Researches on Engineering”, Science and Technology, 4(2),75-88, (2018).
3. [3] Solmaz, H. "A comparative study on the usage of fusel oil and reference fuels in an HCCI engine at different compression ratios." Fuel 273, 117775, (2020).
4. [4] Polat, Seyfi, Ahmet Uyumaz, Hamit Solmaz, Emre Yilmaz, Tolga Topgül, and H. Serdar Yücesu. "A numerical study on the effects of EGR and spark timing to combustion characteristics and NO x emission of a GDI engine." International Journal of Green Energy 13(1), 63-70, (2016).
5. [5] Kocakulak, T., Solmaz, H. “Ön ve son iletimli paralel hibrit araçların bulanık mantık yöntemi ile kontrolü ve diğer güç sistemleri ile karşılaştırılması”, Journal of the Faculty of Engineering & Architecture of Gazi University, 35(4), (2020).
6. [6] Yasin, H., “Modelling and control of hybrid vehicle”, International Journal of Innovative Technology and Interdisciplinary Sciences, 2(3), 212-222, (2019).
7. [7] Ekici, Y. E., Dikmen, İ. C., Nurmuhammed, M., & Karadağ, T. Efficiency Analysis of Various Batteries with Real-time Data on a Hybrid Electric Vehicle. International Journal of Automotive Science And Technology, 5(3), 214-223, (2021).
8. [8] Solmaz, H., Kocakulak, T.. “HCCI Menzil Arttırıcı Motor Kullanılan Seri Hibrit Bir Aracın Modellenmesi”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8(2), 279-292, (2020).

9. [9] Zhuang, W., Li, S., Zhang, X., Kum, D., Song, Z., Yin, G., Ju, F. “A survey of powertrain configuration studies on hybrid electric vehicles”, Applied Energy, 262, 114553, (2020).
10. [10] Borthakur, S., & Subramanian, S. C. “Design and optimization of a modified series hybrid electric vehicle powertrain”, Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering, 233(6), 1419-1435, (2019).
11. [11] Ekici, Y. E., Tan N. “Charge and discharge characteristics of different types of batteries on a hybrid electric vehicle model and selection of suitable battery type for electric vehicles”,International Journal of Automotive Science and Technology, 3(4), 62-70, (2019).
12. [12] Sabri, M. F. M., Danapalasingam, K. A., Rahmat, M. F. A. “Improved fuel economy of through-the-road hybrid electric vehicle with fuzzy logic-based energy management strategy”, International Journal of Fuzzy Systems, 20(8), 2677-2692, (2018).
13. [13] Khajepour, A., Fallah, M. S., Goodarzi, A. “Electric and hybrid vehicles: technologies, modeling and control-a mechatronic approach”, John Wiley & Sons, (2014).
14. [14] Kural, E. and Guvenc, B. A., “Predictive-equivalent consumption minimization strategy for energy management of a parallel hybrid vehicle for optimal recuperation”, Journal of Polytechnic; 18(3), 113-124, (2015).
15. [15] Amini, A., Başlamışlı, Ç., İnce, B. C. “Design of energy management systems for electric/hybrid buses with optimal control methods: case study for the city of ankara and cost analysis Hybrid Buses with Optimal Control Method, Case Study for the City of 2017”, (2017).
16. [16] Amini, A., Önder, E. T., Başlamışlı, S. C., Köprübaşı, K. “Parallel hybrid vehicle fuel consumptıon optimization by equivalent consumption minimization strategy, International Automotive Technologies Congress”, 23-24 May, Bursa, 1-7, (2016).
17. [17] Dinçmen, E., Güvenç, B. A. “A control strategy for parallel hybrid electric vehicles based on extremum seeking”, International Journal of Vehicle Mechanics and Mobility. 50(2), 199-227, (2012).
18. [18] Khajepour, A., Fallah, M. S., Goodarzi, A. “Electric and hybrid vehicles: Technologies, modeling and control-A mechatronic approach”, New Jersey, John Wiley & Sons, (2014).
19. [19] Eğrisöğüt T. A., Kazan, R. “Modeling the dishwasher with fuzzy logic”, Engineer and Machine, 48(565), 3-8. (2007).
20. [20] Panday, A., Bansal, H. O. “A review of optimal energy management strategies for hybrid electric vehicle”, International Journal of Vehicular Technology, 160510, 1-19, (2014).
21. [21] Yılmaz, M., Arslan, E. “Using fuzzy logic to solve geodetic problems”, 2nd Engineering Measurements Symposium, 23-25 November, İstanbul, 512-522, (2005).
22. [22] Liu, W., “Introduction to hybrid vehicle system modeling and control”, John Wiley & Sons, (2013).
23. [23] Cirstea, M., Dinu, A., McCormick, M., Khor, J. G. “Neural and fuzzy logic control of drives and power systems”, UK: Newnes, (2002).
24. [24] Yin, He, Zhou W., Li M., Ma C., Zhaoet C., “An adaptive fuzzy logic-based energy management strategy on battery/ultracapacitor hybrid electric vehicles”, IEEE Transactions on transportation electrification, 2.3: 300-311, (2016).
25. [25] Xu, G., Li, W., Xu, K. and Song, Z., “An intelligent regenerative braking strategy for electric vehicles”, Energies, 4(9), 1461-1477, (2011).
26. [26] Guo, J., Li, W., Wang, J., Luo, Y., Li, K. “Safe and Energy-Efficient Car-Following Control Strategy for Intelligent Electric Vehicles Considering Regenerative Braking”, IEEE Transactions on Intelligent Transportation Systems, (2021).
27. [27] Chandan, S., Alok PS Chauhan., “Modeling and fuzzy logic control of photovoltaic-fuel cell-battery hybrid vehicle”, AIP Conference Proceedings, 2061(1), (2019).
28. [28] Gujarathı, P. K., Shah, V., Lokhande, M. “Fuzzy logic based energy management strategy for converted parallel plug-in hybrid electric vehicle”, IEEE 8th Control and System Graduate Research Colloquium,185-190. (2017).
29. [29] Ming, L. “Energy management strategy of a plug-in parallel hybrid electric vehicle using fuzzy control”, Energy Procedia, 105, 2660-2665, (2017).
30. [30] Dawei, M., Yu, Z., Meilan, Z., Risha, N. “Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle”, Computers and Electrical Engineering, 58, NN 447-464, (2017).
31. [31] Ma, K., Wang, Z., Liu, H., Yu, H., Wei, C. “Numerical investigation on fuzzy logic control energy management strategy of parallel hybrid electric vehicle”, Energy Procedia, 158, 2643-2648, (2019).
32. [32] Chen, P. T., Pai, P. H., Yang, C. J., Huang, K. D. “Development of transmission systems for parallel hybrid electric vehicles”, Applied Sciences, 9(8), 1538, (2019).
33. [33] Chen, B., Li, X., Evangelou, S. “Comparative Study of Hybrid Powertrain Architectures from a Fuel Economy Perspective”, In 14th International Symposium on Advanced Vehicle Control (Vol. 14). AVEC. (2018).
34. [34] Kiyakli, A. O., Solmaz, H. “Modeling of an electric vehicle with MATLAB/Simulink”, International journal of automotive science and technology, 2(4), 9-15, (2018).
35. [35] Kunt, M. A.“Advisor Based Modelling of Regenerative Braking Performance of Electric Vehicles at Different Road Slopes”, International Journal of Automotive Science And Technology, 4(2), 98- 104, (2020).
36. [36] Thallapalli, V. K. T., Kıyaklı, A. O., Kocakulak, T. “Modeling of an Electric Tractor and Determining Energy Consumption Values for Different Duties”, Engineering Perspective, 1 (2): 79-85, (2021).
37. [37] Yimin, G., Ehsani, M., “A torque and speed coupling hybrid drivetrain-architecture, control, and simulation”, IEEE Transactions on Power Electronics, 21(3), 741-748, (2006).
38. [38] Da Silva, S. F., Eckert, J. J., Silva, F. L., Silva, L. C., Dedini, F. G., ”Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization ofenergy consumption,exhaust emissionsand battery degradation”, Energy Conversion and Management, 234, 113909, (2021).
39. [39] Jiang, J., Jiang, Q., Chen, J., Zhou, X., Zhu, S., Chen, T., “Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey”, Wireless Communications and Mobile Computing, (2021)
40. [40] Pipitone, E., Vitale, G. “A regenerative braking system for internal combustion engine vehicles using supercapacitors as energy storage elements-Part 1: System analysis and modelling”, Journal of Power Sources, 448, 227368, (2020).
41. [41] Xu, W., Chen, H., Zhao, H., Ren, B. “Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system”, Mechatronics, 57, 95-108, (2019).
42. [42] Yurdaer E., Kocakulak T. “Comparison of Energy Consumption of Different Electric Vehicle Power Systems Using Fuzzy Logic-Based Regenerative Braking”, Engineering Perspective, 1 (1): 11-21, (2021).
43. [43] Zhao, Q., Zhang, H., Xin, Y. “Research on Control Strategy of Hydraulic Regenerative Braking of Electrohydraulic Hybrid Electric Vehicles”, Mathematical Problems in Engineering, (2021).