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Automatic Control and Computer Sciences

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01-02-2024

Numerical Analysis of Pneumatic Regenerative Braking System

Author: Yongcun Zhu

Published in: Automatic Control and Computer Sciences | Issue 1/2024

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Abstract—

Aimed at understanding the pneumatic regenerative braking system, one simulation model was established based on the basic parameters of the regenerative braking system and validated based on an experimental test bed. Then the effects of air compressor displacement and accumulator volume on the braking energy recovery process were analyzed. For the analysis, the accumulator volume is set as 12 L, the maximum pressure is set as 3 MPa, the air compressor displacement is set as 560 cc r^–1, and the total mass of the vehicle is 1699 kg. The analysis results show that the vehicle needs more than 100 s to stop while the regeneration efficiency is 5.7% when the initial speed is set as 120 km h^–1 and the pneumatic regenerative braking system is applied. Further analysis showed that the air compressor displacement has a great influence on the regenerative braking system. The braking distance increases with the increasing of the accumulator volume while the energy regeneration efficiency reduces with the increasing of the accumulator volume. The braking distance increases with the increasing of accumulator volume. But it has less effect on the regeneration efficiency which can be even ignored.

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Hagin, F. and Merker, P., Drive systems with brake-energy recovery, Int. Automotive Fuel Economy Research Conf. (1st) Proc., 1980, vol. 1, no. 1, pp. 416–423.

Gao, Yi., Chen, L., and Ehsani, M., Investigation of the effectiveness of regenerative braking for EV and HEV, SAE Trans., 1999, vol. 108, pp. 3184–3190. https://doi.org/10.4271/1999-01-2910CrossRef

Khanra, M., Chakraborty, D., and Nandi, A.K., Improvement of regenerative braking energy of fully battery electric vehicle through optimal driving, J. Asian Electr. Vehicles, 2018, vol. 16, no. 1, pp. 1789–1798. https://doi.org/10.4130/jaev.16.1789CrossRef

Manoharan, S., Krishnamoorthy, K., Sathyaseelan, A., and Kim, S.-J., High-power graphene supercapacitors for the effective storage of regenerative energy during the braking and deceleration process in electric vehicles, Mater. Chem. Front., 2021, vol. 5, no. 16, pp. 6200–6211. https://doi.org/10.1039/d1qm00465dCrossRef

Lamichhane, M., Yonjan, S., and Bhetal, N., Design and fabrication of regenerative braking system, Int. J. Eng. Res., 2021, vol. 9, no. 3, pp. 1–6. https://doi.org/10.33329/ijoer.9.3.1CrossRef

Karyś, S. and Stawczyk, P., Cost-effective power converters for small wind turbines, Energies, 2021, vol. 14, no. 18, p. 5906. https://doi.org/10.3390/en14185906CrossRef

Radaš, I., Župan, I., Šunde, V., and Ban, Ž., Route profile dependent tram regenerative braking algorithm with reduced impact on the supply network, Energies, 2021, vol. 14, no. 9, p. 2411. https://doi.org/10.3390/en14092411CrossRef

He, R. and Zhu, S.Y., Regenerative braking fuzzy control considering maximum braking energy recovery power, J. Chongqing Univ. Technol. (Nat. Sci.), 2022, vol. 36, no. 1, pp. 1–11. https://doi.org/10.3969/j.issn.1674-8425(z).2022.01.001CrossRef

Zhang, B., Zhao, G., Huang, Yo., Ni, Ya., and Qiu, M., Optimal energy management for series-parallel hybrid electric city bus based on improved genetic algorithm, Mechanika, 2020, vol. 26, no. 3, pp. 252–259. https://doi.org/10.5755/j01.mech.26.3.24133CrossRef

10.

Li, H., Chu, J.W., Sun, S.H., and Li, H.G., Characteristics of vehicle-mounted electromagnetic coupling flywheel energy storage system, Energy Storage Sci. Technol., 2021, vol. 10, no. 5, pp. 1687–1693. https://doi.org/10.19799/j.cnki.2095-4239.2021.0318CrossRef

11.

Wei, T., Wu, L., Han, L., and Huo, Q., Research on the integrated braking energy recovery strategy based on supercapacitor energy storage for AC-DC-AC variable-frequency drive, Proc. CSEE, 2014, vol. 34, no. 24, pp. 4076–4083. https://doi.org/10.13334/j.0258-8013.pcsee.2014.24.011CrossRef

12.

Fayad, A., Ibrahim, H., Ilinca, A., Sattarpanah Karganroudi, S., and Issa, M., Energy recovering using regenerative braking in diesel–electric passenger trains: Economical and technical analysis of fuel savings and ghg emission reductions, Energies, 2022, vol. 15, no. 1, p. 37. https://doi.org/10.3390/en15010037CrossRef

13.

Zhu, S. and Yao, P.X., Research on hydraulic system for bus braking energy recovery, Mach. Tools Hydraulics, 2017, vol. 45, no. 22, pp. 75–78+86. https://doi.org/10.3969/j.issn.1001-3881.2017.22.020

14.

Bravo, R.R.S., De Negri, V.J., and Oliveira, A.A.M., Design and analysis of a parallel hydraulic–pneumatic regenerative braking system for heavy-duty hybrid vehicles, Appl. Energy, 2018, vol. 225, pp. 60–77. https://doi.org/10.1016/j.apenergy.2018.04.102ADSCrossRef

15.

Kim, J., Kwon, B., Park, Yo., Cho, H., and Yi, K., A control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles, Proc. Inst. Mech. Eng., Part D: J. Automobile Eng., 2022, vol. 236, no. 7, pp. 1546–1567. https://doi.org/10.1177/09544070211038466CrossRef

16.

Wang, J., Huang, J., and Qi, Z.Q., Research on simulation of electronic-controlled pneumatic regenerative braking system, Appl. Mech. Mater., 2013, vols. 278–280, no. 280, pp. 360–364. doi 10.4028/www.scientific.net/amm.278-280.360

17.

Mei, P., Karimi, H.R., Yang, S., Xu, B., and Huang, C., An adaptive fuzzy sliding-mode control for regenerative braking system of electric vehicles, Int. J. Adapt. Control Signal Process., 2022, vol. 36, no. 2, pp. 391–410. https://doi.org/10.1002/acs.3347CrossRef

18.

Xu, W., Chen, H., Zhao, H., and Ren, B., Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system, Mechatronics, 2019, vol. 57, pp. 95–108. https://doi.org/10.1016/j.mechatronics.2018.11.006CrossRef

19.

Gautam, A.K., Tariq, M., Pandey, J.P., and Verma, K.S., Optimal power management strategy by using fuzzy logic controller for BLDC Motor-Driven E-Rickshaw, J. Intell. Fuzzy Syst., 2022, vol. 42, no. 2, pp. 1089–1098. https://doi.org/10.3233/jifs-189774CrossRef

20.

Erhan, K. and Özdemir, E., Prototype production and comparative analysis of high-speed flywheel energy storage systems during regenerative braking in hybrid and electric vehicles, J. Energy Storage, 2021, vol. 43, p. 103237. https://doi.org/10.1016/j.est.2021.103237CrossRef

21.

Župan, I., Šunde, V., Ban, Ž., and Krušelj, D., Algorithm with temperature-dependent maximum charging current of a supercapacitor module in a tram regenerative braking system, J. Energy Storage, 2021, vol. 36, p. 102378. https://doi.org/10.1016/j.est.2021.102378CrossRef

22.

Marquis-Favre, W., Bideaux, E., and Scavarda, S., A planar mechanical library in the AMESim simulation software. Part I: Formulation of dynamics equations, Simul. Modell. Pract. Theory, 2006, vol. 14, no. 1, pp. 25–46. https://doi.org/10.1016/j.simpat.2005.02.006CrossRef

23.

Lynn, A., Smid, E., Eshraghi, M., Caldwell, N., and Woody, D., Modeling hydraulic regenerative hybrid vehicles using AMESim and Matlab/Simulink, Proc. SPIE, 2005, vol. 5805, pp. 24–40. https://doi.org/10.1117/12.603712ADSCrossRef

24.

Marquis-Favre, W., Bideaux, E., and Scavarda, S., A planar mechanical library in the AMESim simulation software. Part II: Library composition and illustrative example, Simul. Modell. Pract. Theory, 2006, vol. 14, no. 2, pp. 95–111. https://doi.org/10.1016/j.simpat.2005.02.007CrossRef

Title: Numerical Analysis of Pneumatic Regenerative Braking System
Author: Yongcun Zhu
Publication date: 01-02-2024
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 1/2024
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411624010127

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