Integration Of AI And Iot In Smart Electric Drives: A Future Perspective
Keywords:
Artificial Intelligence, Internet of Things, Smart Electric Drive, Deep Reinforcement Learning, Steering Control, Torque Management, Latency Reduction, Edge Computing, Autonomous Vehicles, Sensor Fusion
Abstract
The study examines how Artificial Intelligence (AI) and the Internet of Things (IoT) are integrated in the smart electric drive systems and their potential influence on vehicle autonomy, efficiency, and safety. The review of 34 academic sources based on a secondary data-based methodology helped to identify current technologies, system issues, and as an educational future trend. They organize the study in terms of six main themes that include latency reduction, cloud security, interoperability, adaptive steering control, machine learning scalability, and torque management. The results also show that AI technology such as deep reinforcement learning, hybrid control approach, and the predictive analytics models will enhance steering accuracy by 27 percent, torque variance within 5 Nm and provide more accurate real-time decisions in less than 10 milliseconds. Cloud-based control frameworks with IoT-based sensor integrations and edge computing enhance system reactivity even more. The paper finds that the integrative approach across the fields of AI and IoT is the key to the development of the new generation of efficient, secure, and scalable electric vehicles.
References
1. Ahmed, M., Zheng, Y., Amine, A., Fathiannasab, H. and Chen, Z., 2021. The role of artificial intelligence in the mass adoption of electric vehicles. Joule, 5(9), pp.2296-2322.
2. Alahi, M.E.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K. and Mukhopadhyay, S.C., 2023. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), p.5206.
3. Albarella, N., Lui, D.G., Petrillo, A. and Santini, S., 2023. A hybrid deep reinforcement learning and optimal control architecture for autonomous highway driving. Energies, 16(8), p.3490.
4. Ammar, M., Haleem, A., Javaid, M., Bahl, S., Garg, S.B., Shamoon, A. and Garg, J., 2022. Significant applications of smart materials and Internet of Things (IoT) in the automotive industry. Materials Today: Proceedings, 68, pp.1542-1549.
5. Anastasiya, M., Dariya, K. and Zinaida, B., 2025. The Role of AI in Optimizing Steering Feedback and Vehicle Handling for Autonomous Driving Applications.
6. Bai, Z., Hao, P., Shangguan, W., Cai, B. and Barth, M.J., 2022. Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections. IEEE Transactions on Intelligent Transportation Systems, 23(9), pp.15850-15863.
7. Bhargava, A., Bhargava, D., Kumar, P.N., Sajja, G.S. and Ray, S., 2022. Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems. International Journal of System Assurance Engineering and Management, 13(Suppl 1), pp.673-680.
8. Enemosah, A., 2024. Integrating machine learning and IoT to revolutionize self-driving cars and enhance SCADA automation systems. International Journal of Computer Applications Technology and Research, 13(5), pp.42-57.
9. Ghosh, R.K., Banerjee, A., Aich, P., Basu, D. and Ghosh, U., 2022. Intelligent IoT for automotive industry 4.0: Challenges, opportunities, and future trends. Intelligent Internet of Things for healthcare and industry, pp.327-352.
10. Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F. and Madina, P., 2021. Data-driven fault diagnosis for electric drives: A review. Sensors, 21(12), p.4024.
11. Gupta, S., Amaba, B., McMahon, M. and Gupta, K., 2021, May. The evolution of artificial intelligence in the automotive industry. In 2021 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-7). IEEE.
12. Jain, M. and Kulkarni, P., 2022, March. Application of AI, IOT and ML for Business Transformation of The Automotive Sector. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 1270-1275). IEEE.
13. Karthikeyan, M. and Sathiamoorthy, S., 2021. Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle. International Journal of Engineering Trends and Technology, 69, pp.204-208.
14. Kumari, N., Priya, S.K., Kumar, A. and Fogla, A., 2024. Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge. arXiv preprint arXiv:2404.16893.
15. Lang, W., Hu, Y., Gong, C., Zhang, X., Xu, H. and Deng, J., 2021. Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review. IEEE Transactions on Transportation Electrification, 8(1), pp.384-406.
16. Liyanage, M., Porambage, P., Ding, A.Y. and Kalla, A., 2021. Driving forces for multi-access edge computing (MEC) IoT integration in 5G. ICT Express, 7(2), pp.127-137.
17. Madhavaram, C.R., Sunkara, J.R., Kuraku, C., Galla, E.P. and Gollangi, H.K., 2024. The future of automotive manufacturing: Integrating AI, ML, and Generative AI for next-Gen Automatic Cars. International Multidisciplinary Research Journal Reviews-IMRJR, 1, p.010103.
18. Mnyakin, M., 2023. Applications of AI, IoT, and cloud computing in smart transportation: A review. Artificial Intelligence in Society, 3(1), pp.9-27.
19. Mohd Isa, A.A., Hasnan, A.A., Hashim, Z. and Othman, J., 2023. Self-driving car with Artificial Intelligence (AI) technology. Enhancing Innovations In e-Learning For Future Preparation, 5, pp.6-20.
20. Obidov, B., 2024. INTELLIGENT ELECTRIC DRIVE CONTROL SYSTEMS AND ARTIFICIAL INTELLIGENCE: AN IN-DEPTH REVIEW. Web of Discoveries: Journal of Analysis and Inventions, 2(9), pp.1-6.
21. Peng, J., Chen, W., Fan, Y., He, H., Wei, Z. and Ma, C., 2023. Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification, 10(1), pp.392-406. Lee
22. Prathiba, S.B., Raja, G., Dev, K., Kumar, N. and Guizani, M., 2021. A hybrid deep reinforcement learning for autonomous vehicles smart-platooning. IEEE Transactions on Vehicular Technology, 70(12), pp.13340-13350.
23. Reda, A., Benotsmane, R., Bouzid, A. and Vásárhelyi, J., 2023. A hybrid machine learning-based control strategy for autonomous driving optimization. Acta Polytechnica Hungarica, 20(9), pp.165-186.
24. Rehan, H., 2024. Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), pp.239-240.
25. Saleem, H., Riaz, F., Shaikh, A., Rajab, K., Rajab, A., Akram, M. and Al Reshan, M.S., 2022. Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car. Computers, Materials & Continua, 71(2).
26. Tyagi, A.K., Mishra, A.K. and Kukreja, S., 2023, December. Role of Artificial Intelligence Enabled Internet of Things (IoT) in the Automobile Industry: Opportunities and Challenges for Society. In International Conference on Cognitive Computing and Cyber Physical Systems (pp. 379-397). Singapore: Springer Nature Singapore.
27. Vermesan, O., John, R., Pype, P., Daalderop, G., Kriegel, K., Mitic, G., Lorentz, V., Bahr, R., Sand, H.E., Bockrath, S. and Waldhör, S., 2021. Automotive intelligence embedded in electric connected autonomous and shared vehicles technology for sustainable green mobility. Frontiers in Future Transportation, 2, p.688482.
28. Vyas, V. and Shetiya, S.S., 2024. Intelligent Electric Power Steering: Artificial Intelligence Integration Enhances Vehicle Safety and Performance. arXiv preprint arXiv:2412.08133.
29. Wang, L.C., Chen, C.C. and Hsu, C.C., 2022. Applying machine learning and GA for process parameter optimization in car steering wheel manufacturing. The International Journal of Advanced Manufacturing Technology, 122(11), pp.4389-4403.
30. Zhang, M., Chen, K. and Zhu, J., 2023. An efficient planning method based on deep reinforcement learning with hybrid actions for autonomous driving on highway. International Journal of Machine Learning and Cybernetics, 14(10), pp.3483-3499.
31. Zhang, S., 2021. Artificial intelligence in electric machine drives: Advances and trends. Authorea Preprints.
32. Zhu, Z., Gupta, S., Gupta, A. and Canova, M., 2023. A deep reinforcement learning framework for eco-driving in connected and automated hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 73(2), pp.1713-1725.
2. Alahi, M.E.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K. and Mukhopadhyay, S.C., 2023. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), p.5206.
3. Albarella, N., Lui, D.G., Petrillo, A. and Santini, S., 2023. A hybrid deep reinforcement learning and optimal control architecture for autonomous highway driving. Energies, 16(8), p.3490.
4. Ammar, M., Haleem, A., Javaid, M., Bahl, S., Garg, S.B., Shamoon, A. and Garg, J., 2022. Significant applications of smart materials and Internet of Things (IoT) in the automotive industry. Materials Today: Proceedings, 68, pp.1542-1549.
5. Anastasiya, M., Dariya, K. and Zinaida, B., 2025. The Role of AI in Optimizing Steering Feedback and Vehicle Handling for Autonomous Driving Applications.
6. Bai, Z., Hao, P., Shangguan, W., Cai, B. and Barth, M.J., 2022. Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections. IEEE Transactions on Intelligent Transportation Systems, 23(9), pp.15850-15863.
7. Bhargava, A., Bhargava, D., Kumar, P.N., Sajja, G.S. and Ray, S., 2022. Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems. International Journal of System Assurance Engineering and Management, 13(Suppl 1), pp.673-680.
8. Enemosah, A., 2024. Integrating machine learning and IoT to revolutionize self-driving cars and enhance SCADA automation systems. International Journal of Computer Applications Technology and Research, 13(5), pp.42-57.
9. Ghosh, R.K., Banerjee, A., Aich, P., Basu, D. and Ghosh, U., 2022. Intelligent IoT for automotive industry 4.0: Challenges, opportunities, and future trends. Intelligent Internet of Things for healthcare and industry, pp.327-352.
10. Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F. and Madina, P., 2021. Data-driven fault diagnosis for electric drives: A review. Sensors, 21(12), p.4024.
11. Gupta, S., Amaba, B., McMahon, M. and Gupta, K., 2021, May. The evolution of artificial intelligence in the automotive industry. In 2021 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-7). IEEE.
12. Jain, M. and Kulkarni, P., 2022, March. Application of AI, IOT and ML for Business Transformation of The Automotive Sector. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 1270-1275). IEEE.
13. Karthikeyan, M. and Sathiamoorthy, S., 2021. Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle. International Journal of Engineering Trends and Technology, 69, pp.204-208.
14. Kumari, N., Priya, S.K., Kumar, A. and Fogla, A., 2024. Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge. arXiv preprint arXiv:2404.16893.
15. Lang, W., Hu, Y., Gong, C., Zhang, X., Xu, H. and Deng, J., 2021. Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review. IEEE Transactions on Transportation Electrification, 8(1), pp.384-406.
16. Liyanage, M., Porambage, P., Ding, A.Y. and Kalla, A., 2021. Driving forces for multi-access edge computing (MEC) IoT integration in 5G. ICT Express, 7(2), pp.127-137.
17. Madhavaram, C.R., Sunkara, J.R., Kuraku, C., Galla, E.P. and Gollangi, H.K., 2024. The future of automotive manufacturing: Integrating AI, ML, and Generative AI for next-Gen Automatic Cars. International Multidisciplinary Research Journal Reviews-IMRJR, 1, p.010103.
18. Mnyakin, M., 2023. Applications of AI, IoT, and cloud computing in smart transportation: A review. Artificial Intelligence in Society, 3(1), pp.9-27.
19. Mohd Isa, A.A., Hasnan, A.A., Hashim, Z. and Othman, J., 2023. Self-driving car with Artificial Intelligence (AI) technology. Enhancing Innovations In e-Learning For Future Preparation, 5, pp.6-20.
20. Obidov, B., 2024. INTELLIGENT ELECTRIC DRIVE CONTROL SYSTEMS AND ARTIFICIAL INTELLIGENCE: AN IN-DEPTH REVIEW. Web of Discoveries: Journal of Analysis and Inventions, 2(9), pp.1-6.
21. Peng, J., Chen, W., Fan, Y., He, H., Wei, Z. and Ma, C., 2023. Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification, 10(1), pp.392-406. Lee
22. Prathiba, S.B., Raja, G., Dev, K., Kumar, N. and Guizani, M., 2021. A hybrid deep reinforcement learning for autonomous vehicles smart-platooning. IEEE Transactions on Vehicular Technology, 70(12), pp.13340-13350.
23. Reda, A., Benotsmane, R., Bouzid, A. and Vásárhelyi, J., 2023. A hybrid machine learning-based control strategy for autonomous driving optimization. Acta Polytechnica Hungarica, 20(9), pp.165-186.
24. Rehan, H., 2024. Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), pp.239-240.
25. Saleem, H., Riaz, F., Shaikh, A., Rajab, K., Rajab, A., Akram, M. and Al Reshan, M.S., 2022. Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car. Computers, Materials & Continua, 71(2).
26. Tyagi, A.K., Mishra, A.K. and Kukreja, S., 2023, December. Role of Artificial Intelligence Enabled Internet of Things (IoT) in the Automobile Industry: Opportunities and Challenges for Society. In International Conference on Cognitive Computing and Cyber Physical Systems (pp. 379-397). Singapore: Springer Nature Singapore.
27. Vermesan, O., John, R., Pype, P., Daalderop, G., Kriegel, K., Mitic, G., Lorentz, V., Bahr, R., Sand, H.E., Bockrath, S. and Waldhör, S., 2021. Automotive intelligence embedded in electric connected autonomous and shared vehicles technology for sustainable green mobility. Frontiers in Future Transportation, 2, p.688482.
28. Vyas, V. and Shetiya, S.S., 2024. Intelligent Electric Power Steering: Artificial Intelligence Integration Enhances Vehicle Safety and Performance. arXiv preprint arXiv:2412.08133.
29. Wang, L.C., Chen, C.C. and Hsu, C.C., 2022. Applying machine learning and GA for process parameter optimization in car steering wheel manufacturing. The International Journal of Advanced Manufacturing Technology, 122(11), pp.4389-4403.
30. Zhang, M., Chen, K. and Zhu, J., 2023. An efficient planning method based on deep reinforcement learning with hybrid actions for autonomous driving on highway. International Journal of Machine Learning and Cybernetics, 14(10), pp.3483-3499.
31. Zhang, S., 2021. Artificial intelligence in electric machine drives: Advances and trends. Authorea Preprints.
32. Zhu, Z., Gupta, S., Gupta, A. and Canova, M., 2023. A deep reinforcement learning framework for eco-driving in connected and automated hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 73(2), pp.1713-1725.
Published
2024-12-24
How to Cite
Bharathi Y H. (2024). Integration Of AI And Iot In Smart Electric Drives: A Future Perspective . Revista Electronica De Veterinaria, 25(2), 1978-1984. https://doi.org/10.69980/redvet.v25i2.2040
Section
Articles
