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Eco-driving is based on a simple principle: don't just react to what is happening now, but anticipate what is coming next. If a vehicle knows that a steep hill, a bend or a lower speed limit is approaching, it can adjust its speed in advance. This leads to smoother driving and less wasted energy, without noticeably increasing travel times. Because trucks and buses are heavy and sensitive to road conditions, they offer particularly large opportunities for energy savings.

Turning a complex problem into a practical solution

Behind every eco-driving recommendation lies a challenging calculation. The vehicle must take into account acceleration, braking, road conditions and technical limitations, while producing advice fast enough for use during driving. This research of Yannick Heuts shows that these calculations can be made much more efficient. By reformulating the problem into a single optimization framework, reliable solutions can be obtained quickly enough for real-time use inside the vehicle.

Real-world tests confirm the benefits

The concept was tested with a battery-electric bus on a route around Maastricht. The experiments showed that eco-driving also works outside computer simulations. Depending on the conditions, energy consumption was reduced by between 6.5 and 12 percent, while journey times remained virtually unchanged. These results confirm that eco-driving assistance can deliver meaningful savings in everyday operation.

Realism matters

The experiments also highlighted the importance of realistic vehicle models. For example, curves impose limits on speed because of comfort and safety requirements. Ignoring such factors can result in unrealistic driving advice. The study found that incorporating road curvature and carefully calibrating vehicle characteristics leads to more accurate energy predictions and, in some situations, even additional energy savings.

Looking further ahead is more important than more detail

One of the most striking findings is that having a long look-ahead distance is more valuable than using extremely detailed calculations. Knowing what lies further down the road allows the vehicle to make better decisions, while excessive computational detail offers relatively little additional benefit. This means eco-driving systems do not necessarily need enormous computing power to be effective.

Fast algorithms make real-time use possible

Traditional optimization methods are often too slow for use inside a moving vehicle. The dissertation therefore developed a new computational approach that exploits the mathematical structure of the problem. As a result, updated speed advice can be calculated continuously during driving, making practical on-board implementation feasible.

Not everything is under the vehicle's control

Although eco-driving can substantially reduce energy use, external factors remain important. Traffic conditions, road infrastructure and driver behavior all influence the final outcome. This makes testing in realistic conditions essential. Laboratory simulations alone are not enough to guarantee success on the road.

A practical step towards sustainable mobility

The transition to fully emission-free transport will take time, and conventional vehicles will remain part of traffic for many years. According to this research, eco-driving offers an immediate and practical way to reduce energy consumption in both electric and combustion-engine vehicles. By combining accurate models, experimental validation and efficient algorithms, the dissertation demonstrates that smarter driving—not necessarily slower driving—can already make road transport significantly more sustainable.

Title of PhD thesis: . Supervisors: Dr.ir. Tijs Donkers, and Prof. Siep Weiland.