Development of Smart Magnetic Braking Actuator Control for a Heavy Electric Vehicle

Electric actuator; Electric vehicles; Fuzzy; Magnetic

A braking system is one of the most important features in a vehicle. Braking system can be categorized into hydraulic, electric, and mechanical brakes according to (Khurmi and Gupta, 2005). Trucks and buses are considered heavy vehicles that commonly use mechanical brakes in their drum brake design system (Bu and Tan, 2007). The mechanical brake utilizes an air force via a pneumatic actuator to produce linear movement. This action expands the brake shoe to create friction with the wheel drum in order to decelerate the vehicle.

Heavy vehicles provide air generated by a compressor stored in a tank. This compressor is powered by the rotating part of the internal combustion engine (ICE) (Holmberg et al., 2014). Currently, electric vehicles (EVs) have gained popularity due to their potential advantages such as more friendly to the environment and low total cost of ownership compared to conventional ICE vehicles (Sheth and Sarkar, 2019). Instead of ICE, EVs rely heavily on electric motors as their prime mover ( Riba et al., 2016; Eldho Aliasand and Josh, 2020).

However, electric buses that use conventional pneumatic actuators powered by compressors experience increased inefficiency and loss due to the applied conversion stages, namely the compressor, air tank, cylinders, hoses, etc. for braking events (Bendix, 2009). Additionally, the supporting components naturally contribute additional weight to a vehicle. Government regulation PP No 55/2012 states that buses can be categorized into several classes, such as small, medium, big, maxi, tandem, and double-decker, depending on their size and weight (GoI, 2012). Moreover, the rule restricts the allowed weight. For instance, a big bus class (12m or longer) can only have a maximum weight of 16 tons. Therefore, an alternative actuator uses electric power to produce linear movement with the advantage of reduction stages; the weight for braking is also investigated.

Numerous research articles have focused on EV areas, such as the research pertaining to air conditioners using brushless direct current (DC) compressors (Nasruddin and Sinambela, 2015). Particularly, for investigations regarding EV braking, a combinational regenerative and mechanical braking system is studied by (Yusivar et al., 2015). However, the study emphasized the control of combining friction and harnessing energy from the movement of the vehicle. Other types of so-called electric powered braking systems use the principle of electromagnetic force, which can be applied, for instance, in cranes and wheel chairs. Moreover, the electric braking system is also applied for a high-speed train in Germany (Hofmann et al., 2000; Yasa et al., 2016). This study utilizes a magnetic solenoid principle for the electric braking actuator. However, with on/off activation controls, activation results in the sudden movement of the vehicle. This presents a problem, as conventional pneumatic actuator braking systems have different responses to the solenoid system. It follows the rule of pressurized air, as shown in the research conducted by (Yang et al., 2017), and also generates transmission loses (Wang et al., 2017). In order to control the magnetic field to differentiate the breaking intensity, a controller is prepared to generate the signal to the magnetic solenoid. The research objective is to obtain a proper control for smooth braking action by using the magnetic actuator with the integration of an artificially intelligent (AI) method. Furthermore, AI control would improve the performance of the response time of the actuator.

      A model of an electric braking actuator is developed to have the analogous response to the pneumatic braking actuator. Integrating AI method, namely the fuzzy logic control, was applied to generate braking signals to provide smoother curves (similar to the conventional pneumatic actuator response). Thus, abrupt deceleration is prevented using a mixed shape for function. Moreover, the fuzzy controller can improve the time response result and minimize the 30% loss due to the piping-hose in the pneumatic system. In order to implement the results of the braking intensity, the PWM technique manipulates the outcome of the fuzzy controller and uses a sampling period of 1 ms to process the signal, which is dedicated to digitally controlling the magnetic field and pushing the rod for braking action.

    The work of this research was supported by Penelitian Terapan Unggulan Perguruan Tinggi (PTUPT NKB-2949/UN2.RST/HKKP.05.00/2020) of Ristekdikti and Publikasi Unggulan Terindeks Internasional (PUTI NKB-645/UN2.RST/HKP.05.00/2020) Research Grants. Many thanks for all parties at the Universitas Indonesia and Politeknik Negeri Jakarta, which provided facilities and opportunities to this study.   

Acarman, T., Ozguner, U., Hatipoglu, C., Igusky, A.-M., 2000. Pneumatic Brake System Modeling for System Analysis. In: Paper presented at the Truck and Bus Meeting and Exposition, Portland

Badr, M.F., 2018. Modelling and Simulation of a Controlled Solenoid. In: IOP Conference Series: Materials Science and Engineering, 433, 012082. doi:10.1088/1757-899x/433/1/012082

Bendix., 2009. Air Brake Handbook. Ohio, USA: Bendix Commercial Vehicle Systems LLC

Bu, F., Tan, H., 2007. Pneumatic Brake Control for Precision Stopping of Heavy-Duty Vehicles. IEEE Transactions on Control Systems Technology, Volume 15(1), pp. 53–64

Chang, E.-C., Liu, Y.-C., Chang, C.-H., 2019. Experimental Performance Comparison of Various Sliding Modes Controlled PWM Inverters. Energy Procedia, Volume 156, pp. 110–114

Eldho Aliasand, A., Josh, F.T., 2020. Selection of Motor foran Electric Vehicle: A Review. Materials Today: Proceedings, Volume 24, pp. 1804–1815

GoI (Government of Indonesia), 2012. Peraturan Pemerintah No 55 tentang Kendaraan (

Government Regulations No. 55 about Vehicle)

Hofmann, M., Werle, T., Pfeiffer, R., Binder, A., 2000. 2D and 3D Numerical Field Computation of Eddy-current Brakes for Traction. IEEE Transactions on Magnetics, Volume 36(4), pp. 1758–1763

Holmberg, K., Andersson, P., Nylund, N.-O., Mäkelä, K., Erdemir, A., 2014. Global Energy Consumption due to Friction in Trucks and Buses. Tribology International, Volume 78, pp. 94–114

Ida, N., 2015. Engineering Electromagnetics (3^rd ed.): Springer

Imad, M., Hosseini, A., Kishawy, H.A., 2019. Optimization Methodologies in Intelligent Machining Systems - A Review. IFAC-PapersOnLine, Volume 52(10), pp. 282–287

Khurmi, R., Gupta, J., 2005. A Textbook of Machine Design: S Chand & Co Ltd

Kraa, O., Becherif, M., Ayad, M.Y., Saadi, R., Bahri, M., Aboubou, A., Tegani, I., 2014. A Novel Adaptive Operation Mode based on Fuzzy Logic Control of Electrical Vehicle. Energy Procedia, Volume 50, pp. 194–201

Nasruddin, N., Sinambela, H., 2015. Design and Experimental Study of Air Conditioning System using Brushless Direct Current (BLDC) Compressor in National Electric Car. International Journal of Technology, Volume 6(6),  pp. 954–960

Riba, J.-R., López-Torres, C., Romeral, L., Garcia, A., 2016. Rare-earth-free Propulsion Motors for Electric Vehicles: A Technology Review. Renewable and Sustainable Energy Reviews, Volume 57, pp. 367–379

Sheth, A., Sarkar, D., 2019. Life Cycle Cost Analysis for Electric vs. Diesel Bus Transit in an Indian Scenario. International Journal of Technology, Volume 10(1), pp. 105–115

Taghizadeh, M., Ghaffari, A., Najafi, F., 2009. Modeling and Identification of a Solenoid Valve for PWM Control Applications. Comptes Rendus Mécanique, Volume 337(3), pp. 131–140

Wang, Z., Zhou, X., Yang, C., Chen, Z., Wu, X., 2017. An Experimental Study on Hysteresis Characteristics of a Pneumatic Braking System for Muli-Axle Heavy Vehicle in Emergency Braking Situations. Applied Sciences, Volume 7(8), pp. 1–17

Yang, F., Li, G., Hua, J., Li, X., Kagawa, T., 2017. A New Method for Analysing the Pressure Response Delay in Pneumatic Brake System Caused by the Influence of Transmission Pipes.  Applied Sciences, Volume 7(9), pp. 1–20

Yasa, Y., Sincar, E., Ertugrul, B.T., Mese, E.. 2016. A Multidisciplinary Design Approach for Electromagnetic Brakes. Electric Power Systems Research, Volume 141, pp. 165–178

Yu, Z., Mohammed, A., Panahi, I., 1997. A review of three PWM techniques. In: Proceedings of the 1997 American Control Conference (Cat. No.97CH36041)

Yusivar, F., Haslim, H.S., Farabi, Y., Nuryadi, K., 2015. New Control Scheme for Combined Regenerative and Mechanical Brakes in Electric Vehicles. International Journal of Technology, Volume 6(1), pp. 44–52