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Model predictive control based MLP-ANN to enhance tracking response with energy saving of EV drive cycles using
Ahmed M Hassan1,2, Hamada Esmaiel3, Mohammed M Alammar3
1Department of Electrical Power and Machines Engineering, Faculty of Engineering, Benha University, Cairo, Egypt.
Plos One
|February 5, 2026
Summary
This study introduces a new method for electric vehicle (EV) speed tracking control and energy saving using an artificial neural network (ANN) to optimize the drive system. The approach enhances EV performance and battery life.
Area of Science:
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background:
- Electric vehicle drive systems (EVDS) require efficient speed tracking control (STC) and energy saving for optimal performance.
- Interior Permanent Magnet Synchronous Motors (IPMSM) are favored in EVDS for their efficiency, reliability, and power density.
- Traditional control methods may not fully optimize both STC and energy saving simultaneously.
Purpose of the Study:
- To propose and validate a novel STC and energy saving methodology for EVDS.
- To enhance the stability and effectiveness of EV drive system performance.
- To compare online ANN-based tuning with offline optimization techniques for EV control.
Main Methods:
- Utilized a 5-phase interior permanent magnet synchronous motor (IPMSM) within the EVDS.
- Implemented a multilayer perceptron (MLP) artificial neural network (ANN) for online PI controller tuning.
- Employed Model Predictive Control (MPC) for generating gating pulses of the 5-phase voltage source inverter (VSI) to minimize current harmonics and torque ripples.
- Conducted comparative analysis using European drive cycle (ECE-15) and custom IM240 drive cycle, evaluating metrics like overshoot, MSE, IAE, and energy saving.
Main Results:
- The proposed methodology demonstrated superior speed tracking performance for the EVDS.
- Significant energy saving was achieved, validated through drive cycle testing.
- Online MLP-ANN tuning outperformed offline transit search optimization (TSO) in key performance indicators.
- Reduced current harmonics and torque ripples were observed due to MPC implementation.
Conclusions:
- The novel STC and energy saving methodology is effective for EVDS.
- The ANN-based online tuning approach offers advantages over traditional offline methods.
- The proposed system contributes to reduced battery charging costs and extended battery lifespan for electric vehicles.
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