Heavy Goods Vehicles and CO2
13.13 / 15024 / 16044 / 18033
From 2013 to 2018
TML conducted a study on CO2 reduction measures for the freight transport sector for ACEA. Using an integrated approach, TML evaluated measures related to vehicle engine efficiency, vehicle design, vehicle use, traffic management, and CO2 policy.
CO₂ reduction measures
TML conducted a study on CO₂ reduction measures for the freight transport sector for ACEA (European Automobile Manufacturers' Association). Using an integrated approach, we evaluated measures related to vehicle engine efficiency, vehicle design, vehicle use, traffic management, and CO₂ policy. The time frame was 2020 and beyond. Two driving cycles were considered: long-distance and regional delivery.
First came the literature review, after which we conducted a survey of major European truck manufacturers. Their expertise, together with simulations with VECTO (Vehicle Energy Calculation TOol), were used to verify and complete the results of the literature review. Assumptions on the evolution of legislation were made in consultation with ACEA.
For new vehicles, fuel savings of 15 - 17% are possible by 2020. OEMs can achieve more than half of the improvement, with the greatest potential lying in increasing engine efficiency.
| OEM improvement potential by 2020 | Long Haul | Regional delivery |
|---|---|---|
| Engine efficiency | 5.00 % | 4.50 % |
| Auxiliaries management | 1.50 % | 1.70 % |
| Transmission | 0.50 % | 0.50 % |
| Alternative powertrains | N/A | N/A |
| Axles | 0.50 % | 0.50 % |
| Driver assistance system | 2.50 % | 2.50 % |
| Total OEM | - 9.67 % | -9.38 % |
Note: due to the confidential nature of inputs, detailed results of the survey are not included in the published study.
The rest of the new vehicle improvements come from other parties, such as tyre manufacturers and builders of bodies and trailers (aerodynamics).
| Other party improved potential by 2020 | Long Haul | Regional delivery |
|---|---|---|
| Tyres | 4.00 % | 3.00 % |
| Aerodynamics: fairings, tales, etc. | 4.00 % | 3.00 % |
| Weight reduction | 0.50 % | 0.90 % |
| Total others | - 8.30 % | -6.76 % |
When considering the whole fleet, normal vehicle use, and replacement rates (based on 15-year vehicle use), the contribution of these new technologies is limited, but the contribution increases as their diffusion increases. Most of the fuel saving potential lies in optimising vehicle use.
Validation
The proposed materials and methods were validated with a stakeholder group from the European freight transport industry and new findings were added.
The final report was presented in February 2016 at the ACEA event "Reducing CO2 from Road Transport Together".
Scenario analysis
As a third step in this process, TML assessed the CO2 reduction potential of certain measures in heavy road freight transport through a scenario approach, setting conditions in terms of technological progress and diffusion. The list of measures was prepared by the client ACEA. For both long-distance and regional traffic, two scenarios were drawn up in each case: a realistic and an optimistic one. The measures themselves can be grouped under four topics:
- Operation:
- Spreading the use of long, heavy trucks according to the European modular system throughout Europe.
- Driving in platoons.
- Training of drivers.
- Load optimisation.
- Incentives for fleet renewal.
- Vehicles:
- Lowering trailer weight.
- Tyres with low rolling resistance.
- Fuels:
- Refuelling infrastructure for alternative fuels (especially gas).
- Liquid biofuels.
- Infrastructure:
- Vehicle infrastructure communication.
- Road maintenance.
The table below summarises the results of the study. It is important to note that the reduction figures should not be added up. The measures are considered individually and for certain combinations there are (sometimes significant) interactions.
| Measure | Realistic | Optimistic | Cost | Remarks | ||
| Operations | LH | RD | LH | RD | ||
| EMS | 7% | 3.5% | 12% | 6% | Low | 17.5% improvement per individual application 40% market share assumed in realistic LH, 20% in RD |
| Platooning | 1.1% | 2.2% | Moderate | 10% reduction per individual application 11-22% penetration, only in long-haul |
||
| Driver training | 5% | 7% | 7% | 10% | Low | Full penetration assumed Combined effect of driver training and ADAS |
| Load factor optimalisation | 4.1% | 7.8% | 7.8% | 14.4% | N/A | Theoretical calculation! Assuming 5-10 percentage point increase in load factor |
| Fleet renewal incentives | 0.9% | 1.7% | High | Theoretical calculation! Assuming a decrease in age at replacement of 1-2 years Also provides benefits for tailpipe emissions |
||
| Vehicles | ||||||
| Semitrailer lightweighting | 1.0% | 1.3% | High | Only for articulated (long-haul) vehicles - estimates are considered high by stakeholders | ||
| LRRT | 7.5% | 7.5% | 12.5% | 12.5% | Low | 100% penetration of class B (realistic) or class A (optimistic) |
| Fuels | ||||||
| DAFI (natural gas vehicles) | 1% | 1% | 5% | 5% | Moderate | 10-20% improvement per individual application Higher penetration + higher share of renewable gas Also provides benefits for tailpipe emissions |
| Liquid biofuels | 3.3% | 3.3% | 4.5% | 4.5% | N/A | Compared to 2014 fuel mix |
| Infrastructure | ||||||
| V2I ITS | 2% | 3% | 4% | 7% | N/A | Much higher potential in other operations (urban and local) |
| Road maintenance | 3% | 3% | 5% | 5% | Moderate | Benefits also generated for other road users |
