Introduction
Every vehicle that moves on a road consumes energy. That is not a surprise. But where exactly does that energy go? And how much of it is directly tied to your tires? If you're a simulation engineer or a chassis development leader, the answer has direct consequences on the quality of your models and the decisions your team makes from them.
Let's break it down!
What is rolling resistance?
Rolling resistance is the energy lost by a tire as it rolls. Every time a tire rotates, it deforms to conform to the road surface. Put simply, the tire needs to go from a round shape to a flat shape in the contact patch. And because the rubber compounds that make up a tire are viscoelastic materials, (meaning they deform and recover but not instantaneously), part of that energy is lost as heat.
That loss is what we call rolling resistance.
More precisely: rolling resistance is the energy dissipated by the tire per unit of distance traveled. It manifests as a force opposing the direction of motion, the Rolling Resistance Force (RRF or FRR in technical notation).
A simplified but powerful metric captures this: the Rolling Resistance Coefficient (CRR), defined as: CRR = FRR / Z where Z is the vertical load applied on the tire (in newtons).
CRR is dimensionless. It allows you to compare tires independently of vehicle weight. As an order of magnitude, passenger car tires on the market today range from ~around 5 to around 10 kg/t. Low rolling resistance tires, sometimes called "green tires," sit below 6.5 kg/t. Standard tires average around 8 kg/t.
Rolling resistance and regulations:
Rolling resistance is not just a performance metric, it is increasingly a compliance one. Worldwide, regulators are tightening the rules.
In the EU, the tyre labelling system (classes A to E) gives consumers visibility on RR performance, while mandatory limits ban passenger car tyres above approximately 10 kg/t. In the US, NHTSA and DOE set federal rolling resistance and fuel efficiency requirements. Globally, UNECE Regulation R117 defines harmonised test methods and maximum allowable thresholds adopted by many countries.
For chassis teams, this means RR is no longer just an engineering variable. It shapes what tyre specifications are even available for your program.
Where does it come from? The physics behind the phenomenon
The origin is visco-elasticity, a property of rubber compounds that makes them simultaneously elastic (they spring back) and viscous (they dissipate energy). When the tire enters the contact patch, it compresses. When it exits, it doesn't fully restitute the energy. The result: more reaction force at the front of the contact patch than at the rear. This imbalance creates a moment that opposes rotation, and that's your rolling resistance.
Three deformation mechanisms dominate:
- Flexion of the tire crown as it enters the contact zone
- Compression of the tread blocks under vertical load
- Shear of the tread in the contact patch
Two secondary mechanisms also contribute, though to a lesser extent: micro-slippage at the contact interface, and aerodynamic drag from the rotation of the tire itself.
The key takeaway: rolling resistance is not a fixed property. It varies with multiple parameters such as inflation pressure, load, speed, ambient temperature, road surface texture... At speeds below 100-120 km/h, CRR is relatively stable. Above that threshold, it increases with speed, a critical factor for highway-use modeling.
Rolling resistance in the bigger picture: the five forces on a moving vehicle
Rolling resistance doesn't act alone. To understand its real impact, you need to place it in the full picture of vehicle driving resistance, the total set of forces opposing forward motion:
- 1- Rolling resistance forces (FRR) from tire deformation
- 2- Aerodynamic drag forces (Faero) proportional to the square of speed, dependent on vehicle frontal area and drag coefficient (Cx)
- 3- Internal friction forces (Finternal) from differential, wheel hubs, and residual brake drag (approximately 50 N for a mid-size passenger car)
- 4- Gravity forces (Fg) on slopes
- 5- Inertia forces (FInertia) during acceleration and deceleration
The total driving resistance is: FRA = FRR + FAero + Finternal + Fg + FInertia
At highway speeds, aerodynamic forces dominate. In urban driving with frequent stops and accelerations, inertia dominates. Rolling resistance and internal friction, by contrast, are nearly constant regardless of speed or acceleration. They're always there, always consuming energy.
How much does rolling resistance contribute to fuel consumption?
Here's where it gets concrete and consequential.
Depending on driving cycle, rolling resistance accounts for 15 to 20% of a passenger vehicle's total fuel consumption. That's not marginal. That's 1.0 to 1.6 liters per 100 km, on a vehicle consuming around 7-8 L/100 km overall.
With hybrid and electric vehicles being able to recover energy from braking, the importance of tires in overall fuel consumption increases even more, due to energy recovery, heavier vehicle loads, and a higher overall powertrain efficiency. This is especially true in urban and extra-urban driving cycles when energy can be recovered while braking.
For a 1,500 kg passenger car with label C tires (CRR = 8.5 kg/t) and a 75 kW engine, the order of magnitude are the following:
Now switch to low rolling resistance tires (A Label CRR = 6.0 kg/t). The gain is consistent across all cycles: approximately 0.2 to 0.3 L/100 km, or 3 to 5% fuel savings (or 3 to 5% more range in EV), without any other modification to the vehicle.
Want to double that gain? Optimize the transmission ratio alongside the tire swap. By reducing engine RPM by ~5%, you move the engine into a higher-efficiency operating zone, and that same tire improvement can deliver up to 0.2 L/100 km in savings (or again 3 to 5% more EV range), nearly the same baseline gain.
That finding carries an important message: the tire and the powertrain are not independent variables. Simulating one without the other gives you an incomplete answer.
What external factors influence rolling resistance?
The CRR = FRR / Z formula is a useful starting point. But in real operating conditions, it is not enough. CRR is not a fixed value, it shifts with every change in how and where the tyre operates. Here is what actually drives it.
If you're building or using simulation datasets for tire rolling resistance, these parameters matter:
- Inflation pressure: lower pressure increases deformation and increases RR. A well-inflated tire rolls more efficiently.
- Vertical load: higher load increases FRR but usually slightly decreases CRR, though the relationship is not strictly linear.
- Speed: CRR is stable up to ~100-150 km/h, then increases. High-speed highway scenarios require speed-corrected data.
- Temperature: Tires at lower temperature normally exhibit higher Rolling Resistance. Not only is it important to understand how ambient temperature impacts Rolling Resistance, but it is also essential to measure tires at globally stabilized conditions in use to have comparable data.
- Road surface texture: a rough road surface increases energy dissipation. Smooth surfaces reduce RR.
- Tire dimensions: width, diameter, and aspect ratio all influence deformation rates and rolling resistance levels. Tall tires tend to have lower RRc because their large outer diameter is “easier to flatten” in the contact patch.
Each of these variables and their effects need to be correctly captured, or correctly accounted for, in your simulation dataset. An RR value measured in a lab at 80 km/h and 25°C may not represent what happens at 130 km/h on a cold morning. That gap is where unreliable datasets create downstream risk, and why tire model accuracy requires more than lab measurements.
In simulation terms, these are called dependencies, rolling resistance depends on operating conditions, not just tyre design. The datasets delivered by MICHELIN SIMIX are built to capture and describe them accurately, so your simulations reflect what actually happens on the road.
Getting these dependencies right isn't a detail. It's the difference between a model that helps you build an average vehicle and one that helps you build a best-in-class one. And with today's stricter regulations, the stakes are financial too: in Europe, every gram of CO2 above the fleet average target costs a manufacturer €95 per vehicle sold. When rolling resistance is one of the levers that moves that needle, getting your simulation data wrong stops being a technical issue and becomes a budget one.
Rolling resistance in chassis simulation: why the dataset is the foundation
Chassis simulation is only as reliable as the data feeding it. When your model includes tire behavior, and it should, rolling resistance is one of the key parameters that shapes:
- Fuel consumption predictions across driving cycles
- Powertrain sizing decisions (motor torque, gearbox ratios, battery capacity for EVs)
- Thermal models (RR generates heat; that heat affects tire behavior and other vehicle systems)
- Vehicle range calculations, especially critical for electric vehicles
- Benchmark comparisons between design variants
Getting RR wrong by even a few percentage points creates compounding errors. If your team is comparing two tire configurations virtually, or selecting a tire specification to include in a platform program, a dataset that doesn't account for speed, load, and temperature correction isn't giving you a reliable answer. It's giving you an approximation that may look right until it doesn't.
The measurement methodology matters too. International standards (ISO 8767, ISO 9948, SAE J1269, SAE J2452) define how RR should be measured and reported. Understanding which method was used and under what conditions is part of evaluating whether a dataset is fit for your specific simulation context.
From physical measurement to ready-to-use simulation data
Historically, getting reliable RR data meant running physical tests, either deceleration measurements, brake force measurements, or drum/flat-track testing, under controlled conditions, then post-processing the results into a usable format. That takes time. It requires access to test equipment. And it requires someone who knows how to correct for ambient conditions, drum curvature, and speed dependencies.
That's exactly the kind of work that slows teams down and turns engineers into data janitors instead of chassis innovators. It's also where the gap between simulation and reality most often shows up in practice.
The smarter approach: start with simulation-ready datasets that already integrate the physical measurements, the correction factors, and the documented methodology, so your team can focus on what they do best: comparing designs, making decisions, and moving the program forward.
That's what MICHELIN SIMIX is built for. Ready-to-use tire datasets, including rolling resistance data grounded in 30+ years of Michelin tire development, directly compatible with your simulation tools, available without long waits for physical measurements.
Conclusion : key takeaways
- Rolling resistance is the energy lost through tire deformation as the tire rolls. It's caused by the visco-elastic behavior of rubber compounds.
- The Rolling Resistance Coefficient (CRR) is the standard metric. Typical values range from 5.5 kg/t (low RR tires) to 10 kg/t (standard tires).
- Rolling resistance accounts for 15-20% of total vehicle energy consumption across standard driving cycles.
- Switching from standard to low-RR tires saves approximately 3-5% fuel. Combined with transmission optimization, gains can double.
- CRR varies with inflation pressure, load, speed, temperature, and road surface. Reliable simulation datasets must account for these dependencies.
- In chassis simulation, an inaccurate RR dataset creates downstream errors in consumption predictions, powertrain sizing, and range calculations.
- Ready-to-use, real-world-correlated RR datasets let your team skip the data crafting and start simulating with confidence.
