Advisor Based Modeling of the Effect of Rolling Resistance on Regenerative Braking in All-Electric Passenger Cars

Yıl 2019, , 847 - 855, 30.09.2019

Mehmet Akif Kunt

https://doi.org/10.31202/ecjse.603421

Cited By: 2

Öz

In this study, the effect of rolling resistance change on regenerative braking was examined in an all-electric passenger car according to UDDS (Urban Dynamometer Driving Schedule) drive cycle. As the tires with high rolling resistance absorb the kinetic energy of the vehicle more than tires with low rolling resistance, regenerative braking gain is reduced. In the all-electric car model created using the ADVISOR vehicle simulation program state of charge (SOC) was found to be 87.1% in the car with low rolling resistance tires and SOC was 85.6% in the car with high rolling resistance for 1 drive cycle. In addition, regeneration recovery was also investigated due to road slope. As the road slope increased, the amount of recovery was reduced for both tires. While a recovery of 735.01 kJ was achieved in a tire with low rolling resistance, in a tire with high rolling resistance a recovery of 670.85 kJ was achieved at a 5% road slope.

Anahtar Kelimeler

Regenerative braking, rolling resistance, ADVISOR, state of charge

Elektrikli Binek Tipi Otomobillerde Yuvarlanma Direnci Değişiminin Rejeneratif Frenlemeye Etkisi

Öz

Bu çalışmada tümüyle elektrikli binek tipi bir otomobilde UDDS Urban Dynamometer Driving Schedule-Kentsel Dinamometre Sürüş Programı) sürüş çevrimine göre yuvarlanma direnci değişiminin rejeneratif frenlemeye etkisi incelenmiştir. Yuvarlanma direnci yüksek lastiklerin düşük yuvarlanma direncine sahip lastiklere göre taşıtın kinetik enerjisini daha fazla absorbe etmesi sebebiyle rejeneratif frenleme kazanımı azalmıştır. ADVISOR taşıt simülasyon programı kullanılarak oluşturulan tümüyle elektrikli otomobil modelinde 1 sürüş çevrimi için düşük yuvarlanma direncine sahip lastik kullanan otomobilde State of Charge (SOC) %87.1, yüksek yuvarlanma direncine sahip lastik kullanan otomobilde SOC değeri %85.6 olarak bulunmuştur. Ayrıca yol eğimine bağlı olarak rejenerasyon geri kazanımı da araştırılmıştır. Yol eğiminin artmasıyla elde edilen geri kazanım miktarı her iki lastik için azalmıştır. % 5 yol eğiminde yüksek yuvarlanma direncine sahip lastikte 670.85 kJ geri kazanım elde edilirken düşük yuvarlanma direncine sahip lastikte 735.01 kJ geri kazanım elde edilmiştir.

Anahtar Kelimeler

Rejeneratif frenleme, ADVISOR, şarj seviyesi

Kaynakça

• 1. Keskin, A., Gürü, M., Altıparmak, D., Kunt, M.A., “Production of Tall Oil biodiesel and use of B80 as diesel fuel”, Journal of Polytechnic, 2007, 10(4):391-394.
• 2. Leitman, S., Brant, B. “Build your own electric vehicle”, The McGraw-Hill, Second Edition, USA, (2008).
• 3. Huang Q., Li J., Chen Y., “Control of Electric Vehicle”, Urban Transport and Hybrid Vehicles, InTech, Chengdu, (2010).
• 4. Yuan X., Li L., Gou H., Dong T., “Energy and environmental impact of battery electric vehicle range in China”, Applied Energy, 2015, 157:75-84.
• 5. Trb special report 286 – Tires and passenger vehicle fuel economy. Transp ResBoard 2006.
• 6. Suvak H., Erşan K., “Simulation of A Photovoltaic Panel Supported Real Time Hybrid Electric Vehicle”. IEEE Conference Publications, Renewable Energy Research and Aplication (ICRERA) International Conference, Milwaukee, USA, 22 January 2015, 529-534.
• 7. Sorrentino M., Rizzo G., Sorrentino L., “A study aimed at assessing the potential impact of vehicle electrification on grid infrastructure and road-traffic green house emissions”, Applied Energy, 2014, 120:31–40.
• 8. Millo F., Rolando L., Fuso R., Mallamo F., “Real CO2 emissions benefits and end user’s operating costs of a plug-in hybrid electric vehicle”, Applied Energy, 2014, 114:563–71.
• 9. Husain I, Islam MS. “Design, modeling and simulation of an electric vehicle system”, SAE, SAE Technical Paper No. 1999-01-1149, (1999).
• 10. Markel T, Brooker A, Hendricks T, Johnson V, Kelly K, Kramer B, et al., “ADVISOR: a systems analysis tool for advanced vehicle modeling”, Journal of Power Sources, 2002, 110:255-66.
• 11. Xu JW, Zheng L., “Simulation and Analysis of Series Hybrid Electric Vehicle (SHEV) Based on ADVISOR”, International Conference on Measuring Technology and Mechatronics Automation, Changsha City, China, 13-14 March 2010, 1312-1321.
• 12. Kaloko B.S., Soebagio M.H.P., Purnomo M.H., “Design and development of small electric vehicle using MATLAB/Simulink”, International Journal of Computer Applications, 2011, 24:19-23.
• 13. Schaltz E., “Electrical Vehicle Design and Modeling”, Electric Vehicles - Modelling and Simulation, InTech, Shanghai, (2011).
• 14. Mapelli F.L., Tarsitano D., “Modeling of Full Electric And Hybrid Electric Vehicles”, New Generation of Electric Vehicles, INTECH Open Access Publisher, (2012).
• 15. Rashid M.I.M., Danial H., “ADVISOR simulation and performance test of split plug-in hybrid electric vehicle conversion”, Energy Procedia, 2017, 105:1408–1413.
• 16. Suvak H., Erşan K., “The simulation of a full electric vehicle using the city cycle”, International Journal of Automotive Engineering and Technologies, 2016, 5(2):38-46.
• 17. Brooker A., Haraldsson K., Hendricks T., Johnson V., Kelly K., Kramer B., Markel T., O'Keefe M., Sprik S., Wipke K., Zolot M., “ADVISOR Documentation”, National Renewable Energy Laboratory (NREL), April 2002.
• 18. Guzzela L., Sciarretta A., “Vehicle Propulsion Systems”, Springer, Second Edition, USA, (2007).
• 19. Erdem Y, Taci, M. S., “Effect of regenerative braking and power analysis in electric vehicles”, Journal of Current Researches on Engineering, Science and Technology, 2018, 4 (2): 75-88.
• 20. Xu G., Li W., Xu K., Song Z., “An intelligent regenerative braking strategy for electric vehicles”, Energies, 2011, 4 (9): 1461–1477.