Periodic Reporting for period 2 - optiTruck (optimal fuel consumption with Predictive PowerTrain control and calibration for intelligent Truck)
Reporting period: 2018-03-01 to 2019-08-31
The impact of road traffic on energy efficiency is a major global policy concern and has stimulated a substantial body of innovation aimed at improving underlying vehicle and traffic management technologies and informing public policy action. Reduction of fuel consumption and other consumables without increasing emissions is an important challenge for Heavy Duty Vehicles. As today the Euro VI standard foresees testing with a predefined World Harmonised Heavy Duty Transient Cycle, as defined in ECE regulation. The control and calibration of powertrains are optimised under these given conditions.
However, in real driving conditions and real transport missions there are many optimisation possibilities, especially when balancing fuel efficiency and emissions reduction with a specific vehicle application and operating conditions. Further possibilities can also be realised where extensive exploitation of the big data, telematics and communication technologies are integrated into intelligent architecture of the powertrain system through the use of advanced optimisation and calibration techniques.
optiTruck overall objectives and innovative solution
optiTruck brought together the most advanced technologies from powertrain control and intelligent transport systems to achieve a global optimum for fuel consumption as well as other energy sources and consumables while achieving Euro VI emission standards for heavy duty road haulage (40t).
The optiTruck concept is based on the collective use of 10 Innovation Elements (IE). On their own, these innovations have a fairly minimal impact, but together when all are active at the same time they target to reduce greenhouse gas emissions by 10% in average for a typical transport mission.
Economic benefits for the customer/user: The eco-friendliness of the truck equipped with the optiTruck solution might be awarded with incentives that will have a direct economic impact to the users, i.e. fleet operators, and the most direct economic benefit is the fuel use reduction.
Social impact (i.e. well-being effects for the community): Reducing fuel consumption has a direct effect on CO2 emissions, reducing GHGs, but also reducing average pollutant emissions, improving global air quality.
• a conceptual phase: user needs, transport missions & use cases, system requirements & specifications (WP2), architecture & system design (WP3).
• a development phase: to realise the cloud based optimiser (WP4) and the on-board vehicle system (WP5), overall system integration & verification (WP6)
• an evaluation phase (WP7) to develop and perform the demonstration & validation, the impact assessment, and propose a business and technology roll-out plan.
Main results achieved during P2
• optiTruck Innovation Management (D1.3) was a tool used to monitor the development of the Innovation Elements and assess new Innovation leading to one patent application
• All vehicle components & interfaces were realized (D5.4 D5.5 D5.6). On-board functions were completed and verified (D5.7 D5.8 D5.9 D5.10 D5.11)
• The cloud system and algorithms were also implemented and verified (D4.1-D4.5)
• The functionality of optiTruck cloud and truck systems were verified and integrated on the vehicle demonstrator (D6.2).
• National and international demonstration route were organised and in the later was performing real mission between Turkey and Italy transporting IKEA and Electrolux goods. Result of real life test were inconclusive due to poor quality of data (slope, traffic) reported in D7.2.
• optiTruck demonstrated the use of the two extreme models for impact assessment and scaling up, namely the road segmentation model and the HIL/SIL/MIL vehicle model. The former showed that an optimised speed profile alone can save fuel by 3.68%. By a joint effort of all the optiTruck’s innovation elements, the latter model showed a fuel saving of 13.2% (D7.3).
• optiTruck organised a final conference (D8.3) inviting DG-CLIMA and ACEA to discuss future measures to reduce CO2 emissions, and a final webinar to present project results (D8.4)
• 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
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