Auto OEMHow Co-Simulation and Design Exploration Streamline Hybrid Control Development

Long story short

Optimizing a hybrid vehicle’s energy management system used to mean a lot of trial and error. We replaced that guesswork with a smart, automated approach — combining co-simulation and design exploration tools — and cut fuel consumption by 13%. Even better: we eliminated weeks of manual tuning and made the control development process faster, more reliable, and far more scalable.

🔧 “You don’t need to guess when you can simulate and explore intelligently.”
Mathieu Dutré, Managing Director at CTRL engineering

The challenge: Too many parameters, no clear path

Tuning a simple control loop like a PID controller is relatively straightforward. Over time, engineers have developed plenty of guidelines and rules of thumb to get the job done. But things get complicated quickly when dealing with advanced, rule-based control strategies — like the supervisory energy management system of a hybrid hydraulic vehicle.

These control algorithms often rely on a large number of adjustable parameters and target maps. Each of these influences how the system behaves, and they often interact in non-obvious ways. The result? A vast design space where small changes can have a big impact — and no clear path to the optimal configuration. Manually fine-tuning such a controller is not only time-consuming; it’s practically impossible.

Our Approach: Co-simulation meets automated design exploration

At CTRL Engineering, we tackled this challenge by combining the power of simulation with automated design exploration.
First, we created a detailed model of the hybrid powertrain in Simcenter Amesim. This virtual plant simulates how the mechanical, hydraulic, and electrical systems interact. The supervisory control logic was implemented in Simulink, where all tuning parameters could be managed. These two environments were connected in a closed-loop co-simulation setup — allowing us to evaluate vehicle performance (especially fuel consumption) under realistic driving conditions, using a full New European Driving Cycle (NEDC).

Then came the real breakthrough: we integrated HEEDS, an advanced design exploration tool. Instead of manually adjusting parameters and hoping for the best, HEEDS intelligently navigates the control parameter space. It automatically runs co-simulations, measures the resulting performance, and updates its strategy based on previous outcomes. Over several iterations, it homes in on the best-performing controller configuration — all without manual intervention.

Results: 13% less fuel, zero manual tuning

By automating the tuning process, we achieved a 13% reduction in fuel consumption, bringing the vehicle’s performance down to 3.21 l/100 km on the NEDC cycle.
Just as importantly, we significantly reduced the time required for control development. What would have taken weeks of trial-and-error tweaking was now done in a fraction of the time — and with greater confidence in the final result.

Why it matters to you? Smarter workflows unlock hidden performance

This case confirms the value of combining model-based engineering with automated design exploration. Complex control strategies don’t have to mean complex development workflows. By leveraging the right tools, we uncovered correlations between parameters and system behavior that would otherwise remain hidden — leading to better system performance, faster.

At CTRL Engineering, this is how we approach system optimization: not by guessing, but by simulating, analyzing, and iterating — smartly.