Performance Analysis of Smart Speed Variation in Electric Vehicles Using the Combination of Fuzzy Logic Controller
Article Sidebar
Page Numbers:
1289-1302
Digital Object Identifier:
10.58578/ajstea.v3i4.6649
Viewed: 275 times
Downloaded: 106 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:
F. U. Imo1*, Erukpe P. Aluhumile2, C. A. Nwabueze3 
Copyright :
Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
Electric vehicles (EVs) have emerged as a response to the increasing environmental impact of combustion engines and the rising demand for fossil fuels, offering a sustainable alternative to meet the growing transportation needs that underpin economic development. Ensuring the safe operation of EVs on existing road infrastructure, particularly in environments with physical speed breakers, remains a critical concern. Speed bumps are commonly used to prevent collisions due to excessive speeding; however, they often compromise driving comfort and pose safety risks when encountered unexpectedly. This study proposes a smart speed control system for electric vehicles using a fuzzy logic controller, aimed at replacing traditional speed breakers. The system operates by deploying a transmitter at the entry point of a speed-regulated road segment, which sends speed limit data to approaching vehicles equipped with a corresponding receiver. Upon receiving the signal, the vehicle's speed is automatically adjusted to the designated limit. Once the vehicle exits the speed-restricted zone, a new signal allows it to resume normal speed. Developed using MATLAB/Simulink, the fuzzy logic-based control system not only enhances road safety and driving comfort but also contributes to energy efficiency in EVs. The successful implementation of this vehicle-to-infrastructure (V2I) communication model demonstrates the feasibility of intelligent speed regulation, suggesting its integration as a standard feature in future EVs. This approach provides traffic authorities with a proactive means of managing vehicle speed without direct driver intervention.
Citation Metrics:
Downloads
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Asiabar, A. N., & Kazemi, R. (2019). A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control. Proceedings of the Institute of Mechanical Engineers, Part k Journal of Multi body Dynamics, 233(3), 549–567. https://doi.org/10.1177/1464419318807700
Imo, F. U., Nwabueze, C. A. & Muoghalu, C. N. (2024). Performance Evaluation of Forecasted Data and Trained Data in Long-Short Term Memory Prediction of Device To Device Communication in Lora Based Network, International Journal of Innovative Engineering, Technology and Science (IJIETS) www.ijiets.coou.edu.ng, ISSN: 2533-7365 Vol. 8, No.1, Jan 2024.
Imo, F. U., Akaneme, S. A. & Nwabueze, C. A. (2020). Enhancing WCDMA Traffic Capacity Using Adaptive Sectorization, International Journal of Innovative Engineering, Technology and Science (IJIETS) www.ijiets.coou.edu.ng, ISSN: 2533-7365 Vol. -3, No.-2, Oct – 2020
Liu, W., Qi, H., Liu, X., & Wang, Y. (2020). Evaluation of regenerative braking based on single-pedal control for electric vehicles. Frontiers of Mechanical Engineering, 15(1), 166–179.
Li, Y., Zhong, Z., Zhang, K., & Zheng, T. (2019). A car-following model for electric vehicle traffic flow based on optimal energy consumption. Physical A: Statistical Mechanics and Its Application, 533, 122022. https://doi.org/10.1016/j.physa.2019.122022
Nigeria Auto Journal published by the Nigeria Journalists Association NAJA (2023)
Otor, Emmanuel, Uju, I. U, Nwosu, A. W, Imo, F. U. (2024) Enhancing Permanent Magnet Synchronous Motors Performance Characters Using the Combination of Vector Control and Model Predictive Control (IJPREMS) www.ijprems.com , Vol. 04, Issue 09, September 2024, pp : 1264-1276. DOI: https://www.doi.org/10.58257/IJPREMS360755
Vodovozov, V., & Raud, Z. (2021). Review on Braking Energy Management in Electric Vehicles. Energies, 14(5), 1–26 Vehicles. 2019 5th International Conference on Transportation Information and Safety (ICTIS), 1220– 1225.
WHO Global startus Report on road safty( 2018).
Xie, L., Luo, Y., Zhang, D., Chen, R., & Li, K. (2019). Intelligent energy-saving control strategy for electric vehicle based on preceding vehicle movement. Mechanical Systems and Signal Processing, 130, 484– 501. https://doi.org/10.1016/j.ymssp.2019.05.027
Xu, W., Chen, H., Zhao, H., & Ren, B. (2019). Mechatronics Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system
Mechatronics, 57(November2018), 95–108. https://doi.org/10.1016/j.mechatronics.2018.11.006
Xu, Y., Zheng, Y., & Yang, Y. (2021). On the movement simulations of electric vehicles : A behavioral model-based approach. Applied Energy, 283, 116356. https://doi.org/10.1016/j.apenergy.2020.116356
Zeadally, S., Guerrero, J., & Contreras, J. (2020). A tutorial survey on vehicle-to-vehicle communications. Telecommunication Systems, 73(3), 469–489. https://doi.org/10.1007/s11235-019-00639-8
Zhang, Y., Wang, W., Xiang, C., Yang, C., & Peng, H. (2020). A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle. International Journal of Vehicle Mechanics and Mobility, 60(3), 973–997. https://doi.org/10.1080/00423114.2020.1845
Zhang, J., Tang, T., Yan, Y., & Qu, X. (2021). Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging. Applied Energy, 282(PA), 116215. https://doi.org/10.1016/j.apenergy.2020.116215