Longitudinal Dynamics Modeling of an Electric Go-Kart and Analysis of Regenerative Braking under Various Braking Profiles using MATLAB Simulink
DOI:
https://doi.org/10.15282/ijame.22.3.2025.4.0961Keywords:
Electric Go-Kart, Longitudinal Dynamics, Mathematical Modelling, Regenerative BrakingAbstract
Regenerative braking plays a crucial role in optimizing energy efficiency in electric vehicles (EVs), yet its performance is significantly influenced by driving behavior. This study investigates the impact of various braking profiles on regenerative braking effectiveness, with a focus on battery State of Charge (SOC) recovery. As a proof of concept, a control-oriented, first-principles mathematical model of an electric go-kart was developed in MATLAB Simulink using real prototype parameters to simulate longitudinal vehicle dynamics under different throttle, coasting, and braking conditions. In this setup, the vehicle employs a simple Regenerative Braking System using a direct current motor, which is activated when the throttle pedal is released and operates with a constant Back Electromotive Force (EMF) resistance. This mechanism can slow down the vehicle, but a friction brake is still required for a complete stop. Open-loop simulation results show that full-throttle driving achieves a maximum speed of 13.7 m/s, resulting in a 1.16% SOC depletion. During coasting, when regenerative braking is active, a 0.05% SOC gain was recorded over 100 seconds. For braking applications, the highest energy recovery (+0.030% SOC) occurred with a strategy involving 4 seconds of coasting followed by strong braking; however, this induced a high deceleration of -11.7 m/s², potentially affecting ride comfort. In contrast, immediate light braking without coasting resulted in a lower deceleration of -3.4 m/s² but reduced SOC recovery to 0.023%. These results emphasize the trade-off between energy recovery and ride comfort, underscoring the need for intelligent, adaptive braking strategies in future compact EV designs, particularly where integration with autonomous braking systems is anticipated.
References
[1] R. P. Tapaskar, P. P. Revankar, and S. V. Ganachari, “Advancements in battery management systems for electric vehicles: A MATLAB-based simulation of 4S3P lithium-ion battery packs,” World Electric Vehicle Journal, vol. 15, no. 6, pp. 222–248, 2024.
[2] A. F. Challoob, N. A. Bin Rahmat, V. K. A/L Ramachandaramurthy, and A. J. Humaidi, “Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations,” Energy Exploration & Exploitation, vol. 42, no. 1, pp. 341–372, 2024.
[3] Y. Ortiz, P. Arévalo, D. Peña, and F. Jurado, “Recent advances in thermal management strategies for lithium-ion batteries: A comprehensive review,” Batteries, vol. 10, no. 3, pp. 83–103, 2024.
[4] A. Rahmani, M. Dibaj, and M. Akrami, “Recent advancements in battery thermal management systems for enhanced performance of li-ion batteries: A comprehensive review,” Batteries, vol. 10, no. 8, pp. 265–293, 2024.
[5] A. Afzal, R. K. Abdul Razak, A. D. Mohammed Samee, R. Kumar, Ü. Ağbulut, and S. G. Park, “A critical review on renewable battery thermal management system using heat pipes,” Journal of Thermal Analysis and Calorimetry, vol. 148, pp. 8403–8442, 2023.
[6] A. Elgammal, “Energy optimization in EV battery thermal management using model predictive control,” International Journal of Scientific Modern Research and Technology, vol. 4, no. 5, pp. 30–37, 2025.
[7] J. D. O. Velasquez, J. A. G. Moreno, and N. L. D. Aldana, “Experimental platform for studying energy regeneration in electric vehicle powertrains,” Journal of Power Electronics, vol. 24, pp. 1751–1765, 2024.
[8] R. Islam, S. M. S. H. Rafin, and O. A. Mohammed, “Comprehensive review of power electronic converters in electric vehicle applications,” Forecasting, vol. 5, pp. 22–80, 2023.
[9] X. Zhai, H. Liu, W. Sun, and Z. Su, “Research on intelligent energy management strategies for connected range-extended electric vehicles based on multi-source information,” Scientific Reports, vol. 15, no. 1, pp. 12758–12774, 2025.
[10] F. Ji, Y. Pan, Y. Zhou, F. Du, Q. Zhang, and G. Li, “Energy recovery based on pedal situation for regenerative braking system of electric vehicle,” Vehicle System Dynamics, vol. 58, no. 1, pp. 144–173, 2020.
[11] P. Bathala, B. S. Srivasthav, Y. Sai Srinivas Reddy, and K. Deepa, “Estimation of regenerative braking force in electric vehicles for maximum energy recovery,” 2020 IEEE 17th India Council International Conference (INDICON), pp. 1–7, 2020.
[12] S. Heydari, “Maximizing Energy Harvesting in Electric Vehicles through Optimal Regenerative Braking Utilization,” (Doctoral dissertation, University of Nevada, Reno).
[13] M. J. Yang, H. L. Jhou, B. Y. Ma, and K. Ka. Shyu, “A cost-effective method of electric brake with energy regeneration for electric vehicles,” IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 2203–2212, 2009.
[14] J. Valladolid, M. Calle, and A. Guiracocha, “Analysis of Regenerative Braking Efficiency in an Electric Vehicle Through Experimental Tests,” Ingenius, no. 29, pp. 24–31, 2023.
[15] G. A. Chandak and A. A. Bhole, “A review on regenerative braking methodology in electric vehicle,” International Conference Innovations Power Advanced Computing Technologist [i-PACT2017], pp. 1265–1269, 2017.
[16] T. Yabe, K. Akatsu, N. Okui, T. Niikuni, and T. Kawai, “Efficiency improvement of regenerative energy for an EV,” 26th International Electric Vehicle Symposium (EVS26), vol. 1, pp. 634–640, 2012.
[17] H. Faghihian and A. Sargolzaei, “A novel energy-efficient automated regenerative braking system,” Applied Energy, vol. 390, pp. 125746–125760, 2025.
[18] A. K. Ghazali, M. K. Hassan, M. A. M. Radzi, and A. As’arry, “Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency,” Energies, vol. 16, no. 12, 2023.
[19] A. Mamgai, “Regenerative Braking Systems (RBS),” International Journal of Applied Science and Technology, vol. 6, no. 7, pp. 207–210, 2021.
[20] R. Kubaisi, “Adaptive regenerative braking in electric vehicles,” Adaptive Regenerative Braking in Electric Vehicles (Doctoral dissertation, Karlsruher Institut für Technologie (KIT), 2018.
[21] C. Wang, X. Zhao, R. Fu, and Z. Li, “Research on the comfort of vehicle passengers considering the vehicle motion state and passenger physiological characteristics: Improving the passenger comfort of autonomous vehicles,” International Journal of Environmental Research and Public Health, vol. 17, pp. 6821–6840, 2020.
[22] A. K. Ghazali, M. K. Hassan, M. Amran, and M. Radzi, “Optimizing energy harvesting: A gain-scheduled braking,” Energies, vol. 16, pp. 4561–4580, 2023.
[23] C. C. Chan, A. Bouscayrol, and K. Chen, “Electric, hybrid, and fuel-cell vehicles: Architectures and modeling,” IEEE Transactions on Vehicular Technology, vol. 59, no. 2, pp. 589–598, 2010.
[24] D. W. Gao, C. Mi, and A. Emadi, “Modeling and simulation of electric and hybrid vehicles,” Proceedings of the IEEE, vol. 95, no. 4, pp. 729–745, 2007.
[25] F. Zendri, R. Antonello, F. Biral, and H. Fujimoto, “Modeling, identification and validation of an electric vehicle for model-based control design,” International Workshop on Advanced Motion Control, pp. 118–123, 2010.
[26] M. M. Gulzar, B. Sharif, D. Sibtain, L. Akbar, and A. Akhtar, “Modelling and controller design of automotive cruise control system using hybrid model predictive controller,” 15th International Conference on Emerging Technologies, pp. 329–333, 2019.
[27] A. Agrawal et al., “Mathematical modeling of driving forces of an electric vehicle for sustainable operation,” IEEE Access, vol. 11, pp. 95278–95294, 2023.
[28] C. F. Kusuma, B. A. Budiman, and I. P. Nurprasetio, “Simulation method for extended-range electric vehicle battery state of charge and energy consumption simulation based on driving cycle,” ICEVT 2019 - 2019 6th International Conference on Electric Vehicular Technology, pp. 336–344, 2019.
[29] M. R. Wahid, E. Joelianto, and N. A. Azis, “System Identification of Switched Reluctance Motor (SRM) Using Black Box Method for Electric Vehicle Speed Control System,” 2019 6th International Conference on Electric Vehicular Technology, pp. 208–212, 2019.
[30] S. K. Sunori, M. C. Lohani, S. Maurya, M. Manu, S. Arora, A. Mittal, et al., “Model identification of electric vehicle system,” 2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC, pp. 847–851, 2022.
[31] S. Buggaveeti, M. Batra, J. McPhee, and N. Azad, “Longitudinal vehicle dynamics modeling and parameter estimation for plug-in hybrid electric vehicle,” SAE International Journal of Vehicle Dynamics, Stability, vol. 1, no. 2, pp. 289–297, 2017.
[32] T. A. T. Mohd, M. K. Hassan, and W. M. K. A. Aziz, “Mathematical modeling and simulation of an electric vehicle,” Journal of Mechanical Engineering and Sciences, vol. 8, pp. 1312–1321, 2015.
[33] “System-Level Battery Electric Vehicle (BEV) Model.” https://www.mathworks.com/help/sps/ug/system-level-battery-electric-vehicle-model.html#BEVSystemLevelExample-3
[34] V. Vodovozov, Z. Raud, and E. Petlenkov, “Review on braking energy management in electric vehicles,” Energies, vol. 14, no. 15, pp. 4477–4503, 2021.
[35] S. Idros, M. Abdullah, S. F. Toha, and M. S. Md Said, “Modelling of vehicle longitudinal dynamics for speed control,” Journal of Advanced Research in Applied Mechanics, vol. 123, no. 1, pp. 56–74, 2024.
[36] R. Rajamani, Vehicle Dynamics and Control, Second Edi. New York: Springer, 2012.
[37] R. N. Jazar, Vehicle Dynamics: Theory and Application, Fourth Edi. New York: Springer, 2025.
[38] M. Abdullah, M. A. S. Zainuddin, S. Ahmad, and J. A. Rossiter, “Performance comparison between centralized and hierarchical predictive functional control for adaptive cruise control application,” International Journal of Automotive and Mechanical Engineering, vol. 20, no. 4, pp. 10808–10820, 2023.
[39] “Proton e.MAS 7: Weekend SPARK Media Drive and Lunar New Year Celebration with 30% Charging Discounts – Proton e.MAS,” 2025. https://emas.proton.com/proton-e-mas-7-weekend-spark-media-drive-and-lunar-new-year-celebration-with-30-charging-discounts/ (accessed Mar. 26, 2025).
[40] V. Vodovozov, Z. Raud, and E. Petlenkov, “Comparative analysis of intelligent braking controllers for electric vehicles,” Renewable Energy and Power Quality Journal, vol. 20, pp. 55-60, 2022.
[41] S. S. Bhurse and A. A. Bhole, “A review of regenerative braking in electric vehicles,” 7th IEEE Sponsored International Conference on Computation of Power, Energy, Information and Communication, pp. 363–367, 2018.
[42] T. Patil, R. Yadav, A. Mandhare, M. Saggam, and A. Pratap Singh, “Performance improvement of regenerative braking system,” International Journal of Scientific and Engineering Research, vol. 9, no. 5, pp. 161–165, 2018.
[43] P. Cocron, F. Bühler, T. Franke, I. Neumann, B. Dielmann, and J. F. Krems, “Energy recapture through deceleration - regenerative braking in electric vehicles from a user perspective,” Ergonomics, vol. 56, no. 8, pp. 1203–1215, 2013.
[44] J. Biao, Z. Xiangwen, W. Yangxiong, and H. Wenchao, “Regenerative braking control strategy of electric vehicles based on braking stability requirements,” International Journal of Automotive Technology, vol. 22, no. 2, pp. 465 – 473, 2021.
[45] S. Heydari, P. Fajri, M. Rasheduzzaman, and R. Sabzehgar, “Maximizing regenerative braking energy recovery of electric vehicles through dynamic low-speed cutoff point detection,” IEEE Transactions on Transportation Electrification, vol. 5, no. 1, pp. 262 – 270, 2019.
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