Enhancing Field Oriented Control of Electric Drives with tiny Neural Network

EDGE AI POD

Ever wondered how the electric vehicles of tomorrow will squeeze every last drop of efficiency from their batteries? The answer lies at the fascinating intersection of artificial intelligence and motor control.
The electrification revolution in automotive technology demands increasingly sophisticated control systems for permanent magnet synchronous motors - the beating heart of electric vehicle propulsion. These systems operate at mind-boggling speeds, with control loops closing every 50 microseconds (that's 20,000 times per second!), and future systems pushing toward 10 microseconds. Traditional PID controllers, while effective under steady conditions, struggle with rapid transitions, creating energy-wasting overshoots that drain precious battery life.
Our groundbreaking research presents a neural network approach that drastically reduces these inefficiencies. By generating time-varying compensation factors, our AI solution cuts maximum overshoots by up to 70% in challenging test scenarios. The methodology combines MatWorks' development tools with ST's microcontroller technology in a deployable package requiring just 1,700 parameters - orders of magnitude smaller than typical deep learning models.
While we've made significant progress, challenges remain. Current deployment achieves 70-microsecond inference times on automotive-grade microcontrollers, still shy of our ultimate 10-microsecond target. Hardware acceleration represents the next frontier, along with exploring higher-level models and improved training methodologies. This research opens exciting possibilities for squeezing maximum efficiency from electric vehicles, turning previously wasted energy into extended range and performance. Curious about the technical details? Our complete paper is available on arXiv - scan the QR code to dive deeper into the future of smart motor control.

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1. Enhancing Field Oriented Control of Electric Drives with tiny Neural Network (00:00:00)

2. Introduction to the workshop topic (00:00:07)

3. Fixed function AI in motor control (00:01:02)

4. Permanent magnet synchronous motors explained (00:03:45)

5. PID controllers and their limitations (00:07:59)

6. Neural network solution methodology (00:09:24)

7. Performance results and future challenges (00:13:24)

8. Conclusion and Q&A invitation (00:16:33)