Energy-efficient Control Strategy for an Electric Tricycle for Handicapped Persons under an Urban Drive Cycle

Authors

  • Najmuddin Jamadar
  • Piyush Pawar
  • Ashish Anil Jadhav
  • Rupesh Rajay Ghadigaonkar
  • Kedar Jagannath Detake

DOI:

https://doi.org/10.46610/IJMEEIA.2026.v02i01.004

Keywords:

Assistive mobility, Battery state of charge, Eco predictive control, Electric tricycle, Electric vehicle control, Energy optimization, Regenerative braking, Urban drive cycle

Abstract

Electric tricycles provide an efficient mobility solution for handicapped and elderly individuals in urban environments. However, frequent acceleration and deceleration in city drive cycles lead to increased energy consumption and battery stress. In this work, an eco-predictive control strategy is proposed for improving the energy efficiency of an electric tricycle operating under an urban drive cycle. A detailed longitudinal vehicle model incorporating rolling resistance, aerodynamic drag, gradient force, motor power dynamics and battery state of charge variation is developed. The proposed controller modifies the speed reference using a smoothing filter to reduce aggressive acceleration demand while maintaining acceptable tracking performance. Simulation results demonstrate approximately 3.97 % reduction in total energy consumption, 7.74 % reduction in peak power demand and improved final state of charge compared to a conventional PI controller. The proposed strategy enhances range and reduces battery stress without requiring additional hardware, making it suitable for low-cost assistive electric mobility systems.

References

J. Li, A. Fotouhi, Y. Liu, and Z. Chen, “Review on eco-driving control for connected and automated vehicles,” Renewable and Sustainable Energy Reviews, vol. 189, p. 114025, 2024.

H. He, Y. Guo, and J. Sun, “Eco driving control for intelligent electric vehicle with real-time energy-aware strategy,” Electronics, vol. 10, no. 21, p. 2613, 2021.

V. Mariani, G. Rizzo, F. A. Tiano, and L. Glielmo, “A model predictive control scheme for regenerative braking in vehicles with hybridized architectures,” Control Engineering Practice, vol. 123, p. 105142, 2022.

B. Long, S. T. Lim, Z. F. Bai, J. H. Ryu, and K. T. Chong, “Energy management and control of electric vehicles using hybrid power source in regenerative braking operation,” Energies, vol. 7, no. 7, pp. 4300–4315, 2014.

E. M. Szumska, “Regenerative braking systems in electric vehicles: A review,” Energies, vol. 18, no. 10, p. 2422, 2025.

C. Hampali, S. B. Patil, and R. K. Kulkarni, “Design and development of solar electric tricycle,” Energy Reports, vol. 7, pp. 532–539, 2021.

S. R. Patil and A. K. Sharma, “Electric tricycle for physically challenged person,” International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), vol. 6, no. 8, pp. 14821–14826, 2020.

R. Baz, M. A. Ahmed, and K. S. Khalil, “Self-tuning fuzzy PID control for electric vehicle speed and regenerative energy recovery,” in Proc. IEEE International Conference on Power Electronics and Drive Systems, pp. 356–361, 2022.

P. Suanpang, “Optimization of regenerative braking using Q-learning techniques,” Decision Making: Applications in Management and Engineering, vol. 8, no. 1, pp. 45–58, 2025.

M. H. Sulaiman, N. A. Rahim, and H. Hizam, “Electric vehicle battery state of charge estimation using hybrid data-driven and physical models,” IEEE Access, vol. 13, pp. 21455–21466, 2025.

S. Hou and X. Zhao, “Hierarchical model predictive control based energy management for electric vehicles,” Applied Energy, vol. 362, p. 121877, 2026.

Published

2026-03-13

Issue

Section

Articles