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optimal fuel consumption with Predictive PowerTrain control and calibration for intelligent Truck

Periodic Reporting for period 1 - optiTruck (optimal fuel consumption with Predictive PowerTrain control and calibration for intelligent Truck)

Reporting period: 2016-09-01 to 2018-02-28

• Challenge and context
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
The goal of optiTruck is to bring together the most advanced technologies from powertrain control and intelligent transport systems in order to achieve a global optimum for consumption of fuel (at least 20% reduction) 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 Innovative Elements (IE). On their own, these innovations have a fairly minimal impact, but together when all innovations are active at the same time they will help to reduce greenhouse gas emissions by up to 20% 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.
The optiTruck project is organised in eight Work Packages (WPs), beside Project Management (WP1) and Dissemination, Communication and Liaison (WP8), six technical WPs are the core of the work contributing to the three project phases:
• a conceptual phase during which WP2 identifies user needs, defines transport missions and use cases, establishes system requirements and specifications. Then, the optiTruck overall architecture and system design is developed in WP3
• a development phase with the development of the optiTruck predictive cloud based optimiser (WP4) and simultaneously the on-board vehicle system and its units (WP5), which are then integrated (WP6) into the predictive cloud optimiser and the on-board vehicle system in a global optimiser respectively, before a verification of the overall system
• finally an evaluation phase (WP7) develops and performs the optiTruck demonstration validation and impact assessment, develops business and technology roll-out plan;

During this first Period of activity from September 2016 to February 2018, optiTruck finalised its conceptual phase, started the development phase and defined testing and evaluation plans. All Work Packages progressed well and according to plan although some deliverables were submitted late as the focus was on achieving the technical work on time more than reporting. This approach had no impact on the work progress and all period 1 deliverables and milestones were achieved.

Main results achieved during the first period
Main overall results achieved during this first period are listed hereafter:
• optiTruck Innovation Management Plan is defined (D1.2) and is used to follow the progress of the Innovation Element development
• optiTruck use cases (D2.1) system architecture (D2.2) System & User requirements (2.3) are defined
• optiTruck overall system architecture is agreed and defined(D3.1) in cooperation with WP4 and WP5
• optiTruck design phase was finalised by delivering the Design of data flows and interfaces (D3.2) the On-board predictive system design (D3.3) and the Predictive environment and traffic system design (D3.4)
• The connectivity unit establishing the interface between the cloud optimizer, the on-board systems and optimization functions was implemented (D5.1) and the Real-time capable engine & aftertreatment system thermodynamic models were developed and are presented in D5.5.
• the verification plan for verifying the functionality of optiTruck cloud and truck systems is ready (D6.1) and the validation plan was also prepared as an internal document.
• D8.2 present the proceedings of the first Stakeholder Forum organised as a webinar reporting a wider scope of possible powertrain optimisation including also hybrid solutions.
optiTruck aims to contribute significantly towards the creation of new knowledge and the expansion of current knowledge and State-of-the-Art (SoA). Nine SoA areas were identified. Progress during the first period made in six areas is shortly presented hereafter:
SoA-1: Optimisation of powertrain control and calibration according to real-world conditions
Predictive cruise control scenarios have been simulated within the developed vehicle simulation environment.
SoA-3: Optimized aftertreatment controls
Real-time capable aftertreatment (including Diesel Oxidation Catalyst (DOC), Diesel Particle Filter (DPF), and Selective Catalytic Reduction (SCR)) system model has been developed which can be used for predictive powertrain control function development.
SoA-4: Predictive management and control of auxiliary systems
Real-time capable auxiliary system model including (including fan, water pump, alternator, and air conditioning) has been developed which enables the use of predictive information for optimizing the activation of the aforementioned components
SoA-5: Predictive thermal management
A thermal model has been developed which can run faster than real-time such that future predictions can be made based on this model and hence, allows for advanced cooling system control function development.
SoA-8: Driver support information system (ecoNavigation or ecoDriving for trucks)
An informative Android application has been designed and is developed to assist the driver both visually and auditory while driving.
SoA-9: Definition of the transport mission and initial optimum calibration points
An algorithm has been developed to determine the optimal route that a truck has to follow in order to reach its destination minimizing the energy request and optimizing the route velocity profiles for the pre-mission planning.