The MODALES project, which came to an official close earlier this year, just released two comprehensive factsheets summarising the main findings of the project. MODALES built a user-centric approach to contribute to the greening of the transport sector and address the important issue of reducing emissions from road vehicles. To address vehicle emissions from all sources, including powertrain/exhaust pipe, brakes and tyres, the project researched, developed and tested innovative and complementary solutions encompassing the driver, on-board diagnostics (OBD), periodic inspections and retrofits.
The first factsheet focuses on the ‘Driver’ aspect of the project, which explored driving behavioural aspects and their influence on emissions. MODALES deployed innovative tools and approaches to encourage drivers to adopt more environmentally friendly driving behaviour and reduce pollutant emissions, such as nitrogen oxide (NOx), particulate matter (PM) and particle number (PN)from powertrains, brakes and tyres. Real-condition trials measured emissions using a range of simulation tools and models to assess the correlation of behaviour with emissions. The results were translated into guidelines for drivers and a scoring system was used to provide personalised recommendations. Training videos were produced and tested on volunteer drivers in the trial sites of the project, for drivers of private cars, taxi/light duty vehicles, and heavy-duty vehicles. During the trials, a driving assistant app developed as part of the project collected data on the user’s driving patterns and provided feedback and advice to encourage a more environmentally friendly style of driving. To complement the videos and app, an online awareness campaign was launched to promote simple and effective advice to reduce driving emissions to a wider audience. The effect of these tools on the drivers’ performance was studied during real-world trials with 150 volunteers in seven regions in Europe, as well as a limited trial of 20 drivers in China.
The second factsheet covers the areas of On-Board Diagnostics (OBD), Periodic Technical Inspections (PTI) and Retrofits for diesel vehicles. MODALES focused on producing legal recommendations for a broader use of OBD, which could lead to higher rates of detection of poorly maintained and/or tampered vehicles with increased exhaust emissions. The data accessible from OBD and third-party devices could also be used to identify driver behaviour and its impact on emissions. In the area of Periodic Technical Inspections, project partners investigated the detection of tampering or malfunctions in vehicles and examined a wide range of technical, behavioural and legal criteria to clarify the current and future capabilities of European OBD (EOBD) protocol. Software for passenger cars was created and demonstrated to detect potential tampering violations or improper maintenance. MODALES also looked into the feasibility and potential of retrofit emission controls. Starting from existing systems, different retrofit technologies and prototype technologies were tested and analysed to assess their feasibility and effectiveness in reducing emissions.
“MODALES was a complex project looking at technical, behavioural and legal aspects that influence emissions from internal combustion engine road vehicles, as well as short-term approaches to reduce them. In particular, the project generated new knowledge on the factors that affect brake and tyre emissions as well as from the vehicle exhaust. A range of different tools and recommendations were developed for low emission driving, and better inspection and maintenance of vehicles, which can be taken forward by public authorities, transport operators, driving schools and technology providers,” says Andrew Winder, Senior Manager at ERTICO and MODALES Project Coordinator.
You can discover the results of the project in the two factsheets here. For more detailed results, you can also read Deliverable D5.1 “Publishable Final Report” (available here under WP1 Project Management).
The MODALES project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 815189.
