ABSTRACT:
In order to increase drivetrain fidelity and adaptability in MATLAB/Simulink environments, this research attempts to create a next-generation electric vehicle (EV) simulation framework that blends artificial intelligence (AI) with physics-based modeling. Neuro-adaptive machine learning algorithms were used in the design of a modular AI-Integrated Drivetrain Emulator (AIDE), which dynamically modifies torque, battery state-of-charge (SOC), and regenerative braking in response to driving patterns, terrain, and environmental conditions. AIDE is a reliable simulation tool for intelligent EV system design in both academic and industrial contexts. The suggested system showed a 22% improvement in drivetrain response accuracy and a 17% increase in energy efficiency when compared to traditional static models.1
Cite this article:
Vivek Ghulaxe. AIDE: AI-Integrated Drivetrain Emulator for MATLAB-Simulated Electric Vehicle Ecosystems. Research Journal of Engineering and Technology. 2025; 16(2):47-7. doi: 10.52711/2321-581X.2025.00005
Cite(Electronic):
Vivek Ghulaxe. AIDE: AI-Integrated Drivetrain Emulator for MATLAB-Simulated Electric Vehicle Ecosystems. Research Journal of Engineering and Technology. 2025; 16(2):47-7. doi: 10.52711/2321-581X.2025.00005 Available on: https://ijersonline.org/AbstractView.aspx?PID=2025-16-2-1
12. REFERENCE:
1. K. Song, L. Wei, and Y. Jin, “Adaptive energy efficiency control strategies: trade-offs,” Energy, 2023.
2. K. Patel and R. Nayak, “Electric mobility training and simulation tools using Simulink,” Educ Inf Technol (Dordr), 2023.
3. V. Ghulaxe, “Neuro-Adaptive AI for Dynamic Distraction Mitigation in Autonomous Vehicle Environments,” International Journal of Artificial Intelligence Applications, 2024.
4. X. Chen, L. Zhao, and S. Yu, “Collaborative vehicle simulation using Federated Reinforcement Learning,” Neural Comput Appl, 2024.
5. H. Raza, B. Kumar, and K. Patel, “Electric vehicle simulations using cross-domain benchmarking,” Simul Model Pract Theory, 2023.
6. L. Zhang, T. Lin, and H. Zhou, “Comparative metrics in simulated versus physical EV testbeds,” IEEE Sens J, 2024.
7. R. Haque, T. Dey, and J. Hossain, “Electric vehicle simulation platforms: A comprehensive overview,” J Clean Prod, 2023.
8. V. Ghulaxe, “Driving the Future: The Role of Artificial Intelligence in Autonomous Vehicles,” International Journal of Engineering Technology Research and Management, 2024.
9. Z. Feng, Y. Wang, and H. Tan, “Comparative analysis of electric vehicle powertrain models,” Energies (Basel), 2024.
10. C. Liu, W. Zhou, and J. Xie, “Modular simulation frameworks for EVs using Python and Simulink,” IEEE Des Test, 2024.
11. Z. Huang and F. Sun, “Integration of AI and Simulink-based EV simulations for smart city deployment,” IEEE ITSC 2023 Proceedings, 2023.
12. M. T. Xu and Anwar, “AI-optimized torque control in PMSM drive systems for electric mobility,” Sensors, 2023.
13. M. Ahmed, Y. Chen, and H. Wang, “A hybrid deep learning approach for accurate SOC estimation in lithium-ion batteries under dynamic conditions,” IEEE Transactions on Industrial Electronics, 2023.
14. S. T. Kim and Zhang, “Simscape-based modeling of electric vehicle dynamics and systems,” Machines, 2023.
15. D. Singh and M. Narang, “Model-based design of electric vehicle drivetrains with AI-enhanced torque control using MATLAB/Simulink,” Simul Model Pract Theory, 2023.
16. B. Luo, K. Li, and H. Jiang, “Incorporating smart inverters into electric mobility networks,” Renewable & Sustainable Energy Reviews, 2024.
17. P. Jiang, M. Tan, and Y. Luo, “Energy recovery models in electric vehicles that consider terrain,” Mech Syst Signal Process, 2023.
18. Y. Ge, R. Huang, and X. Lin, “Incorporating thermal modeling into EV simulation environments,” Appl Therm Eng, 2023.
19. S. Das and A. Singh, “Simulation and fault-tolerant control in electric vehicle drivetrains,” Electric Power Systems Research, 2023.
20. H. Chen, Z. Zhou, and S. Yao, “Graph-based control strategies in hybrid EV powertrains,” Inf Sci (N Y), 2024.
21. M. J. White and Thomas, “Latency metrics in embedded AI control for EVs,” IEEE Embed Syst Lett, 2023.
22. S. Tang, J. Zhang, and M. Zhou, “Deep Q-learning networks for multi-objective optimization of EV drivetrain control,” IEEE Transactions on Intelligent Transportation Systems, 2023.
23. H. Park and C. Lee, “Reinforcement learning for adaptive regenerative braking in electric vehicles,” IEEE Transactions on Industrial Electronics, 2023.
24. Q. Li, Y. Sun, and M. Yao, “Model predictive control and LSTM for adaptive energy management of electric vehicles,” IEEE Trans Veh Technol, 2023.
25. R. F. Ahmed and Islam, “AI-powered SOC estimation methods for electric vehicles,” IEEE Trans Smart Grid, 2023.
26. M. X. Zhang and Zhou, “Comparison of rule-based and AI-driven SOC control systems,” IEEE Transactions on Intelligent Transportation Systems, 2023.
27. A. T. Omar and Hassan, “State-of-charge prediction under stochastic loads in electric vehicles,” Energy Reports, 2023.
28. X. Han, M. Liu, and H. Dong, “Energy recovery and braking analysis in urban EV drive cycles,” Transportation Research Part C, 2024.
29. T. Farooq, F. Khan, and S. Imran, “Evaluating predictive modeling frameworks in electric vehicle simulators,” Simulation Modeling Practice and Theory, 2024.
30. S. A. Raza and Khalid, “Performance metrics for simulation accuracy in EV systems,” IEEE Access, 2024.
31. Z. Li, Y. Duan, and Q. Yan, “AI-based EV traction system simulation results,” International Journal of Vehicle Performance, 2024.
32. Y. Bai, L. Wang, and H. Zhou, “Energy-efficient control strategy for EV regenerative braking using AI-based predictive models,” Energy Convers Manag, 2024.
33. R. B. Chakraborty and Roy, “Torque optimization under variable terrain conditions,” Automotive Innovation, 2024.
34. D. Wu, G. Yang, and K. Chen, “Evaluation of simulation fidelity for electric vehicle torque control systems,” IEEE Transactions on Mechatronics, 2024.
35. Y. Feng, Z. Meng, and X. Li, “Controller response time analysis in real-time EV simulations,” IEEE Transactions on Industrial Electronics, 2024.
36. Y. Zhao, J. Liu, and H. Zhang, “Deep reinforcement learning-based digital twin-based intelligent control framework for EV powertrains,” Appl Energy, 2024.