Compressor Air Pressure and Brake Shield Distance Brake to Braking Accuracy on Brakes Antilock Braking System
DOI:
https://doi.org/10.51278/ajse.v1i1.419Keywords:
Compressor Air Pressure, Braking Accuracy, Antilock Braking SystemAbstract
The development of automotive technology to date is very fast. In the city, it is almost rare to find conventional cars. Most are already using modern technology. The hallmark of modern cars is that they are not fully mechanical. Already combined with several electric and pneumatic tools. Until now the development in the field of chassis is endless. Its initial development began with the discovery of ABS (Anti Lock Brake System) brakes. Basically, ABS is followed by supporting components such as EBD, ESP, and so on.All of this is actually inseparable from the main function of braking. Namely so that the car can run well,, deceleration and stop as desired appropriately. The parts of the brakes that are most influential are the brake shoes and discs and the compressed air from the brakes. Also the most influential accuracy is the distance of the brake shroud. Therefore, in this study the aims of 1. To find the effect of pressure and brake shroud distance. 2. What is the minimum air pressure and brake shroud for the brakes to work properly (grip). This research uses experimental design method. The data is processed with the Minitab program. Data processing shows that: 1. The braking speed is influenced by the amount of compressed air pressure. Partially the distance of the brake shroud does not affect the braking speed. But the interaction of pressure and shroud distance has an effect on braking speed. 2. Air pressure that can be used as a fast ABS braking process is 3.75 – 4.25 (bar) with a shroud distance of 5 (mm).
Keywords: Compressor Air Pressure, Braking Accuracy, Antilock Braking System
References
[2] X. Liu, L. Yu, S. Zheng, Z. Lu, and J. Song, “Research on Redundant Anti-lock Braking Algorithm Based on eBooster,” Qiche Gongcheng/Automotive Eng., vol. 44, no. 1, 2022, doi: 10.19562/j.chinasae.qcgc.2022.01.011.
[3] M. Jundulloh and I. N. Sutantra, “Pemodelan dan Analisa Antilock Braking System (ABS) Pada Military Vehicle Studi Kasus Panser Anoa APC 6x6,” J. Tek. ITS, vol. 6, no. 2, 2018, doi: 10.12962/j23373539.v6i2.26629.
[4] M. N. Nemah, “Modelling and Development of Linear and Nonlinear Intelligent Controllers for Anti-lock Braking Systems (ABS),” J. Univ. Babylon Eng. Sci., vol. 26, no. 3, 2018, doi: 10.29196/jub.v26i3.597.
[5] Z. Yu, B. Shi, L. Xiong, and Q. Shu, “Design and Optimization of Regenerative Braking Strategy Based on Distributed Drive Electric Vehicle,” Tongji Daxue Xuebao/Journal Tongji Univ., vol. 48, no. 11, 2020, doi: 10.11908/j.issn.0253-374x.20234.
[6] R. Raveendran, K. B. Devika, and S. C. Subramanian, “Learning-based fault diagnosis of air brake system using wheel speed data,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng., 2021, doi: 10.1177/09544070211063719.
[7] P. Shah, S. K. Labh, and S. P. Adhikari, “Study on Performance of Vehicle Anti-lock Braking System with Fuzzy Logic controller in Matlab/Simulink,” Int. J. Sci. Res. Eng. Dev., vol. 3, no. 2, 2020.
[8] Y. Yang, G. Li, and Q. Zhang, “A pressure-coordinated control for vehicle electro-hydraulic braking systems,” Energies, vol. 11, no. 9, 2018, doi: 10.3390/en11092336.
[9] N. Wang, S. Wang, Z. Peng, W. Song, X. Xue, and S. B. Choi, “Braking control performances of a disk-type magneto-rheological brake via hardware-in-the-loop simulation,” J. Intell. Mater. Syst. Struct., vol. 29, no. 20, 2018, doi: 10.1177/1045389X18800395.
[10] Y. C. Chen, C. H. Tu, and C. L. Lin, “Integrated electromagnetic braking/driving control of electric vehicles using fuzzy inference,” IET Electr. Power Appl., vol. 13, no. 7, 2019, doi: 10.1049/iet-epa.2018.5817.
[11] D. Viramgama, “Intelligent Vehicle Safety Technology: Anti-lock Braking System (ABS) and its Advancements,” Int. Res. J. Eng. Technol., 2019.
[12] N. Sridhar, K. B. Devika, S. C. Subramanian, G. Vivekanandan, and S. Sivaram, “Antilock brake algorithm for heavy commercial road vehicles with delay compensation and chattering mitigation,” Veh. Syst. Dyn., vol. 59, no. 4, 2021, doi: 10.1080/00423114.2019.1697457.
[13] J. Kim, B. Kwon, Y. Park, H. J. Cho, and K. Yi, “A control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng., 2021, doi: 10.1177/09544070211038466.
[14] Q. Xu, C. Zhou, H. Huang, and X. Zhang, “Research on the coordinated control of regenerative braking system and abs in hybrid electric vehicle based on composite structure motor,” Electron., vol. 10, no. 3, 2021, doi: 10.3390/electronics10030223.
[15] V. Dankan Gowda, A. C. Ramachandra, M. N. Thippeswamy, C. Pandurangappa, and P. Ramesh Naidu, “Modelling and performance evaluation of anti-lock braking system,” J. Eng. Sci. Technol., vol. 14, no. 5, 2019.
[16] R. Kumar, Divyanshu, and A. Kumar, “Nature Based Self-Learning Mechanism and Simulation of Automatic Control Smart Hybrid Antilock Braking System,” Wirel. Pers. Commun., vol. 116, no. 4, 2021, doi: 10.1007/s11277-020-07853-7.
[17] S. I. Haris, F. Ahmad, M. H. C. Hassan, A. K. M. Yamin, and N. R. M. Nuri, “Self Tuning PID Control of Antilock Braking System using Electronic Wedge Brake,” Int. J. Automot. Mech. Eng., vol. 18, no. 4, 2021, doi: 10.15282/IJAME.18.4.2021.15.0718.
[18] F. Ahmad, S. Amri Mazlan, K. Hudha, H. Jamaluddin, and H. Zamzuri, “Fuzzy fractional PID gain controller for antilock braking system using an electronic wedge brake mechanism,” Int. J. Veh. Saf., vol. 10, no. 2, 2018, doi: 10.1504/IJVS.2018.094154.
[19] E. F. Bassey and K. M. Udofia, “Modelling of automatic car braking system using fuzzy logic controller,” Niger. J. Technol., vol. 38, no. 4, 2019, doi: 10.4314/njt.v38i4.27.
[20] M. Aguado-Rojas, W. Pasillas-Lepine, A. Loria, and A. De Bernardinis, “Acceleration Estimation Using Imperfect Incremental Encoders in Automotive Applications,” IEEE Trans. Control Syst. Technol., vol. 28, no. 3, 2020, doi: 10.1109/TCST.2019.2896199.
[21] M. H. A. Al-Mola, M. Mailah, and M. A. Salim, “Applying Active Force Control with Fuzzy Self-Tuning PID to Improve the Performance of an Antilock Braking System,” Int. J. Nanoelectron. Mater., vol. 14, no. Special Issue ISSTE, 2021.
[22] Synopsys, “The 6 Levels of Vehicle Autonomy Explained,” Synopsys Automot., vol. 0, 2021.
[23] H. Mirzaeinejad, “Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network,” Appl. Soft Comput. J., vol. 70, 2018, doi: 10.1016/j.asoc.2018.05.043.
[24] Y. Lu, B. W. Xu, J. W. Wu, and B. Guo, “Research on Hardware-in-the-Loop Test of Bus Antilock Brake System Based on Fuzzy PID,” Jiliang Xuebao/Acta Metrol. Sin., vol. 39, no. 2, 2018, doi: 10.3969/j.issn.1000-1158.2018.02.05.
[25] J. P. Fernandez, M. A. Vargas, J. M. V. Garcia, J. A. C. Carrillo, and J. J. C. Aguilar, “Coevolutionary Optimization of a Fuzzy Logic Controller for Antilock Braking Systems under Changing Road Conditions,” IEEE Trans. Veh. Technol., vol. 70, no. 2, 2021, doi: 10.1109/TVT.2021.3055142.
