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A few years ago, building a complete Battery State of Charge (SOC) Estimation model using a Kalman Filter in MATLAB/Simulink was a huge task.
I still remember spending 2 to 4 weeks on:
- Understanding battery equations
- Designing system architecture
- Creating Simulink blocks
- Tuning Kalman Filter parameters
- Debugging signals and validation
- Running multiple simulations for accuracy
Back then, this process required deep manual effort, patience, and countless hours of testing. But today, something unbelievable happened.
Using the MATLAB Agentic Toolkit, I generated a functional SOC Estimation Simulink Model using Kalman Filter in just 2 minutes. ⏱️🔥
Yes… a task that once took weeks was completed in minutes.
This is not about replacing engineers.
This is about empowering engineers to move faster, think bigger, and focus more on innovation rather than repetitive setup work. 🚀
The future of Model-Based Development is changing rapidly:
- AI-assisted Simulink design
- Faster algorithm prototyping
- Automatic model generation
- Rapid validation workflows
- Smarter debugging and optimization
As engineers, we are entering an era where productivity is multiplying exponentially. The real skill now is not only “coding everything manually” — it’s also knowing how to collaborate effectively with AI tools.
What excites me most is imagining where this will lead in the automotive and EV industry:
- Faster BMS development
- Smarter SDV architectures
- Intelligent control systems
- Rapid prototyping cycles
The engineering world is evolving faster than ever, and I’m excited to keep learning, building, and adapting with these technologies. AI + Engineering is going to redefine the future of development. And honestly, we are only getting started.
Link: matlab/matlab-agentic-toolkit: The MATLAB Agentic Toolkit brings trusted MATLAB capabilities to AI …
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