optiTruck created new knowledge and deepened current knowledge and State-of-the-Art. Main contributions during period 2 are:
• new emissions coordinator function created to optimize the engine operation towards a minimum fuel consumption while respecting legislative emission limits. .
• controls of real-time capable aftertreatment systems optimised for real-world conditions.
• controls of the activation of real-time capable auxiliary system models optimised, using predictive information.
• thermal model developed to run faster than real-time to support with prediction real-time optimisation of cooling system control.
• Energy flow coordinator functions developed including cooling system optimizer, pneumatic system, electric system and air condition optimizers .
• algorithms developed with IPG Truck-Maker to simulate takeover & truck platooning strategies leading to fuel saving.
• Based on truck fleet operator experience, mobile app developed to assist the driver visually & acoustically, providing recommended driving speeds, the scheduled and arrival times.
optiTruck system impact
• Technology impact: use of real time models leading to more accurate results, use of long range data ahead of the vehicle enabling better prediction beyond today's systems.
• Social/environment impact: based on Codognotto partner fleet profile, a gradual introduction of the optiTruck system will lead to CO2 emissions reduction by 12% within 3 yrs. On a larger scale scenario (EU >16t fleet), CO2 reduction will reach 25.5Mt in a period of 5 yrs.
• Economy impact: trucks equipped with optiTruck system can have only a 2 Month payback period; Cloud system and optiTruck simulator are also product economically profitable (D7.4)
• Transport business impact: mental shift of the driver is key for the success of the proposed system in order to be accepted and effective in its use. This requires driver training and HMI support.
Conclusions of the action
• optiTruck demonstrated in simulation the potential of reducing fuel consumption by an average of 13.2% combining predictive ITS data and in-vehicle prediction
• Quality of cloud data is key for the success of this combined approach (traffic, weather, slope data)
• optiTruck results were obtained by calibrated simulation as real demonstrator faced data quality issues and driver unacceptance
• optiTruck results / developments are marketable and will be included in Ford Otosan trucks to enter European market within 5 years
Lessons learned
At the end the project, the optiTruck consortium agreed on the following lessons learned to consider when developping a final product based on our innovations:
ITS data service/map data quality have direct impact on usability of developed solutions
• check slope data accuracy
• derive a global confidence level of the optimised speed profile
In vehicle algorithm strategy and flexibility depending on ITS cloud data
The demonstration route was too ambitious, too long and too complex to prepare
Truck driver did not follow instructions to use the system
• understand the driver acceptance / driver needs
• inform & train carefully the driver about the use of the system
