ISSN (Online): 2812-9709
Journal Menu
Archive
Last Edition
Archive
Vol.5, No.1, 2026: pp.39-50
Experimental Investigation on Electric Vehicle Braking System Using Machine Learning for Enhanced Performance and Safety
Authors:
Lalit N. Patil1
, Gulab D. Siraskar2
, Dipak S. Patil3
, Nikhil Shinde4
,
Vikash K. Agrawal5
1Army Institute of Technology, Dighi, Pune, India
2PCET’s Pimpri Chinchwad College of Engineering and Research, Pune, India
3 PVG’S College of Engineering and Technology & G. K. Pate (Wani) Institute of Management, Pune, India
4Vishwakarma University, Pune, India
5Dr. D. Y. Patil Institute of Technology, Pune, India
Received: 27 June 2025
Revised: 22 February 2026
Accepted: 20 March 2026
Published: 31 March 2026
Abstract:
The rapid increase in the popularity of Electric Vehicles (EVs) can be attributed to their environmental benefits and innovations. Regenerative braking is a braking method considered a very important safety feature in EVs. This study presents an empirical evaluation of an EV braking system, where close attention is paid to the implementation of machine learning (ML) methods to maximize operational efficiency and enhance safety. Practical driving tests were conducted, and sensor data of various parameters, such as vehicle speed, brake pedal pressure, motor torque, and battery charge, were recorded. Various machine learning algorithms were tested, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forests, for predicting braking distance and optimizing the combination of regenerative and friction braking. The results indicate the immense potential of machine learning to maximize braking efficiency, reduce wear and tear on friction brakes, and improve overall safety in EVs.
Keywords:
Electric vehicle, Braking system, Machine learning, Artificial neural networks
References:
[1] W. Li, X. Wang, X. Leng, M. Wang, Modeling and simulation of automobile braking system based on kinetic energy conversion. 2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China, 3–5 September 2008: 1–3.
https://doi.org/10.1109/VPPC.2008.4677741
[2] J.P. Vasconez, M. Viscaino, L. Guevara, F.A. Cheein, A fuzzy-based driver assistance system using human cognitive parameters and driving style information. Cognitive Systems Research, 64, 2020: 174–190.
https://doi.org/10.1016/j.cogsys.2020.08.007
[3] L.N. Patil, H.P. Khairnar, J.A. Hole, D.M. Mate, A.V. Dube, R.N. Panchal, V.B. Hiwase, An Experimental Investigation of Wear Particles Emission and Noise Level from Smart Braking System. Evergreen, 9(3), 2022: 711–720.
[4] H. Jamil, S.S.A. Naqvi, N. Iqbal, M.A. Khan, F. Qayyum, F. Muhammad, S. Khan, D.-H. Kim, Analysis on the driving and braking control logic algorithm for mobility energy efficiency in electric vehicle. Smart Grids and Sustainable Energy, 9, 2024: 12. https://doi.org/10.1007/s40866-023-00190-1
[5] A.M. Amiri, A. Sadri, N. Nadimi, M. Shams, A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers. Accident Analysis & Prevention, 138, 2020: 105468. https://doi.org/10.1016/j.aap.2020.105468
[6] B.S. Kaloko, Soebagio, M.H. Purnomo, Design and Development of Small Electric Vehicle using MATLAB/Simulink. International Journal of Computer Applications, 24(6), 2011: 19–23. https://doi.org/10.5120/2960-3940
[7] H.A. Dağıstanlı, An Integrated Fuzzy MCDM and Trend Analysis Approach for Financial Performance Evaluation of Energy Companies in Borsa Istanbul Sustainability Index. Journal of Soft Computing and Decision Analytics, 1(1), 2023: 39–49.
https://doi.org/10.31181/jscda1120233
[8] J. Abdo, M. Nouby, D. Mathivanan, K. Srinivasan, Reducing disc brake squeal through FEM approach and experimental design technique. International Journal of Vehicle Noise and Vibration, 6(2–4), 2010: 230–246.
https://doi.org/10.1504/IJVNV.2010.036688
[9] Y. Ba, W. Zhang, Q. Wang, R. Zhou, C. Ren, Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system. Transportation Research Part C: Emerging Technologies, 74, 2017: 22–33.
https://doi.org/10.1016/j.trc.2016.11.009
[10] M. del C. Pardo-Ferreira, J.C. Rubio-Romero, F.C. Galindo-Reyes, A. Lopez-Arquillos, Work-related road safety: The impact of the low noise levels produced by electric vehicles according to experienced drivers. Safety Science, 121, 2020: 580–588. https://doi.org/10.1016/j.ssci.2019.02.021
[11] F. Bhatti, M.A. Shah, C. Maple, S.U. Islam, A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments. Sensors, 19(9), 2019: 2071. https://doi.org/10.3390/s19092071
[12] J. Zhou, J. Sun, L. He, Y. Ding, H. Cao, W. Zhao, Control oriented prediction of driver brake intention and intensity using a composite machine learning approach, Energies, 12(13), 2019: 2483. https://doi.org/10.3390/en12132483
[13] J. Vimala, P. Mahalakshmi, A.U. Rahman, M. Saeed, A customized TOPSIS method to rank the best airlines to fly during COVID-19 pandemic with q-ROMFS information. Soft Computing, 27(20), 2023: 14571–14584.
https://doi.org/10.1007/s00500-023-08976-2
[14] J. Wu, H. Hu, Q. Li, S. Wang, J. Liang, Simulation and experimental investigation of a multi-pole multi-layer magnetorheological brake with superimposed magnetic fields. Mechatronics, 65, 2020: 102314.
https://doi.org/10.1016/j.mechatronics.2019.102314
[15] A.A. Agbeleye, D.E. Esezobor, S.A. Balogun, J.O. Agunsoye, J. Solis, A. Neville, Tribological properties of aluminium-clay composites for brake disc rotor applications. Journal of King Saud University – Science, 32(1), 2017: 21–28.
https://doi.org/10.1016/j.jksus.2017.09.002
[16] V.K. Agrawal, L.N. Patil, K.V. Chavan, U.D. Nimbalkar, A computational analysis of heat transfer in solid and vented disc brakes: CFD simulation and thermal performance assessment. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7, 2024: 4735–4749. https://doi.org/10.1007/s41939-024-00400-y
[17] Z. Prakash, Integration of AI and ML in regenerative braking for electric vehicles: a review. Frontiers in Artificial Intelligence, 8, 2025: 1626804. https://doi.org/10.3389/frai.2025.1626804
[18] D. Berjoza, V. Pirs, I. Jurgena, Research into the regenerative braking of an electric car in urban driving. World Electric Vehicle Journal, 13(11), 2022: 202. https://doi.org/10.3390/wevj13110202
[19] Y. Yang, X. Tan, C. Meng, The multi-fuzzy soft set and its application in decision making. Applied Mathematical Modelling, 37(7), 2013: 4915–4923. https://doi.org/10.1016/j.apm.2012.10.015
[20] A. Lebkowski, Temperature, overcharge and short-circuit studies of batteries used in electric vehicles. Przegląd Elektrotechniczny, 1(5), 2017: 67–75. https://doi.org/10.15199/48.2017.05.13
[21] E. Abotalebi, D.M. Scott, M.R. Ferguson, Can Canadian households benefit economically from purchasing battery electric vehicles?. Transportation Research Part D: Transport and Environment, 77, 2019: 292–302.
https://doi.org/10.1016/j.trd.2019.10.014
[22] K. Das, R. Kumar, A. Krishna, Analyzing electric vehicle battery health performance using supervised machine learning, Renewable and Sustainable Energy Reviews, 189, 2024: 113967. https://doi.org/10.1016/j.rser.2023.113967
[23] M.U. Ali, A. Zafar, S.H. Nengroo, S. Hussain, M. Junaid Alvi, H.-J. Kim, Towards a smarter battery management system for electric vehicle applications: A critical review of lithium-ion battery state of charge estimation. Energies, 12(3), 2019: 446. https://doi.org/10.3390/en12030446
[24] Z. Peng, Z. He, Optimization of regenerative braking control strategy for dual-motor electric vehicles based on deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 2025: 1–14.
https://doi.org/10.1109/tits.2025.3553875
[25] V.A. Kalhapure, H.P. Khairnar, Analytical and experimental investigation on wear performance of disc brake pad. Tribology in Industry, 42(3), 2020: 345–362. https://doi.org/10.24874/ti.852.02.20.05
[26] Z.M. Ali, F. Jurado, F.H. Gandoman, M. Ćalasan, Advancements in battery thermal management for electric vehicles: Types, technologies, and control strategies including deep learning methods. Ain Shams Engineering Journal, 15(9), 2024: 102908. https://doi.org/10.1016/j.asej.2024.102908
[27] K. Boudmen, A. El Ghazi, Z. Eddaoudi, Z. Aarab, M.D. Rahmani, Electric vehicles, the future of transportation powered by machine learning: A brief review. Energy Informatics, 7(1), 2024: 80. https://doi.org/10.1186/s42162-024-00379-3
[28] L. Maffei, M. Masullo, Electric Vehicles and Urban Noise Control Policies. Archives of Acoustics, 39(3), 2015: 333–341. https://doi.org/10.2478/aoa-2014-0038
[29] J. Nasar, P. Hecht, R. Wener, Mobile telephones, distracted attention, and pedestrian safety. Accident Analysis & Prevention, 40(1), 2008: 69–75. https://doi.org/10.1016/j.aap.2007.04.005
[30] H. He, C. Wang, H. Jia, X. Cui, An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle. Applied Energy, 259, 2020: 114172.
https://doi.org/10.1016/j.apenergy.2019.114172
[31] N.C. Basjaruddin, K. Kuspriyanto, S. Suhendar, D. Saefudin, V.A. Azis, Hardware simulation of automatic braking system based on fuzzy logic control. Journal of Mechatronics, Electrical Power, and Vehicular Technology, 7(1), 2016: 1–6.
https://doi.org/10.14203/j.mev.2016.v7.1-6
[32] B. Prasanth, R. Paul, D. Kaliyaperumal, R. Kannan, Y. Venkata Pavan Kumar, M. Kalyan Chakravarthi, N. Venkatesan, Maximizing regenerative braking energy harnessing in electric vehicles using machine learning techniques. Electronics, 12(5), 2023: 1119. https://doi.org/10.3390/electronics12051119
© 2026 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 5
Number 1
March 2026.
How to Cite
L.N. Patil, G.D. Siraskar, D.S. Patil, N. Shinde, V.K. Agrawal, Experimental Investigation on Electric Vehicle Braking System Using Machine Learning for Enhanced Performance and Safety. Advanced Engineering Letters, 5(1), 2026: 39-50.
https://doi.org/10.46793/adeletters.2026.5.1.4
More Citation Formats
Patil, L.N., Siraskar, G.D., Patil, D.S., Shinde, N., & Agrawal, V.K. (2026). Experimental Investigation on Electric Vehicle Braking System Using Machine Learning for Enhanced Performance and Safety. Advanced Engineering Letters, 5(1), 2026: 39-50.
https://doi.org/10.46793/adeletters.2026.5.1.4