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Scania Truck Driving Simulator Key Crack

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.

Scania Truck Driving Simulator Key Crack

A benchmark problem for fuel efficient control of a truck on a given road profile has been formulated and solved. Six different solution strategies utilizing varying degrees of off-line and on-line computations are described and compared. A vehicle model is used to benchmark the solutions on different driving missions. The vehicle model was presented at the IFAC AAC2016 symposium and is compiled from model components validated in previous research projects. The driving scenario is provided as a road slope profile and a desired trip time. The problem to solve is a combination of engine-, driveline- and vehicle-control while fulfilling demands on emissions, driving time, legislative speed, and engine protections. The strength of this publication is the collection of all six different solutions in one paper. This paper is intended to provide a starting point for practicing engineers or researchers who work with optimal and/or model based vehicle control.

Thus, the developed powertrain model and parameterization procedure provide a rapid non- invasive way of modelling powertrains of test cars. The parameterizing procedure has been used to model a front wheel drive Golf V with a 1.4L multi-fuel engine and a manual gearbox. The achieved results show a good match between simulation results and test data. The powertrain model has also been tested in real-time in a driving simulator.

Rollover has for long been a major safety concern for trucks, and will be even more so as automated driving is envisaged to becoming a key element of future mobility. A natural way to address rollover is to extend the capabilities of current active-safety systems with a system that intervenes by steering or braking actuation when there is a risk of rollover. Assessing and predicting the rollover is usually performed using rollover indices calculated either from lateral acceleration or lateral load transfer. Since these indices are evaluated based on different physical observations it is not obvious how they can be compared or how well they reflect rollover events in different situations.

Plug-in Hybrid Electric Vehicles (PHEV) provide a promising way of achieving the benefits of the electric vehicle without being limited by the electric range, but they increase the importance of the supervisory control to fully utilize the potential of the powertrain. The winning contribution in the PHEV Benchmark organized by IFP Energies nouvelles is described and evaluated. The control is an adaptive strategy based on a map-based Equivalent Consumption Minimization Strategy (ECMS) approach, developed and implemented in the simulator provided for the PHEV Benchmark. The implemented control strives to be as blended as possible, whilst still ensuring that all electric energy is used in the driving mission. The controller is adaptive to reduce the importance of correct initial values, but since the initial values affect the consumption, a method is developed to estimate the optimal initial value for the controller based on driving cycle information. This works well for most driving cycles with promising consumption results. The controller performs well in the benchmark; however, the driving cycles used show potential for improvement. A robustness built into the controller affects the consumption more than necessary, and in the case of altitude variations the control does not make use of all the energy available. The control is therefore extended to also make use of topography information that could be provided by a GPS which shows a potential further decrease in fuel consumption.

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagins minimum principle-derived optimization tool HOT.

In recent years the need for testing, calibration and certification of automotive components and powertrains have increased, partly due to the development of new hybrid concepts. At the same time, the development within electrical drives enables more versatile chassis dynamometer setups with better accuracy at a reduced cost. We are developing a new chassis dynamometer laboratory for vehicle research, aiming at extending a recently commercially available dynamometer, building a new laboratory around it, and applying the resulting facility to some new challenging vehicle research problems. The projects are enabled on one hand by collaboration with the dynamometer manufacturer, and on the other hand on collaboration with automotive industry allowing access to relevant internal information and equipment. The test modes of the chassis dynamometer are under development in a joint collaboration with the manufacturer. The laboratory has been operational since September 2011 and has already been used for NVH-analysis for a tire pressure indication application, chassis dynamometer road force co-simulation with a moving base simulator, co-surge modeling and control for a 6-cylinder bi-turbo engine, and traditional engine mapping. We are also looking at projects with focus on look-ahead control, as well as clutch and transmission modeling and control, and driving cycle related research.

Hybridization is a promising and obvious way of reducing fuel consumption in automotive applications, however, its ability to reduce emissions in long haulage trucks is not so obvious. The complexity of the powertrain is also increased which makes well designed control systems needed to fully utilize the potential benefits of the hybridization. In this paper, a control strategy that takes advantage of the complex structure of the powertrain in a hybrid electric long haulage truck is developed and evaluated. The control system is based on equivalent consumption minimization strategy where an equivalence factor is used to compare fuel and battery power so that an optimal distribution of power between the components in the powertrain can be calculated. The proposed control system is evaluated in a driving scenario using a model of a complete hybrid electric truck, including an aftertreatment system, and the results are compared with a conventional, non-hybrid, vehicle. The hybridization leads to 31 % lower NOx emissions, primarily due to better thermal conditions in the exhaust system during braking, and at the same time, the fuel consumption was reduced by 3.8 % compared to the non-hybrid vehicle. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Todays vehicle industry is converging more and more to electrification of vehicles, introducing electrical architectures to cooperate side by side with the combustion engine. This paper investigates the potential of using an electric turbocharger in a long haulage application during highway driving. A charge sustainable control strategy is developed, implemented, tuned, and evaluated on a heavy duty truck model. The benefits of using an electrical turbocharger on a heavy duty diesel truck, from a long haulage perspective, are evaluated. By calibrating the implemented controller, long haulage driving routes can be charge sustainable and consume less fuel than a conventional truck with fix turbine geometry, the fuel savings for the simulated case is 0.9%. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

A benchmark problem for fuel efficient control of a truck with engine, driveline, and chassi models on a given mission with a road topography profile is formulated. The Vehicle model is provided with open access to the vehicle model equations and parameters. It is compiled from model components validated in previous research projects and the result is a non-linear model that contains mixed continuous and discrete control variables. The driving; scenario is provided as road slope profile and a desired trip time. The problem to solve is a combination of engine-, driveline- and Vehicle-control while fulfilling demands on emissions, driving time, legislative speed, and engine protections. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.


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