Train Simulator 2015 Crack Free 62 [HOT]
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Amtrak is the largest passenger railroad service in America, offering daily intercity trains to hundreds of destinations across the contiguous United States, as well as several cities in Canada. Every Amtrak train comes equipped with comfortable seats with extra legroom, as well as several four-seat areas with tables in the middle. In addition, each car has a freshly cleaned restroom, free WiFi and power outlets at every seat. There are also snack bars where you can purchase drinks or food during your journey. On average, there are 8 Amtrak trains from Chicago to Los Angeles every day, as well as 13 trips on the weekends, with prices starting from $146.
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.
The automotive industry is facing a major challenge to reduce environmental impacts. As a consequence, the increasing diversity of powertrain configurations put a demand on testing and evaluation procedures. One of the key tools for this purpose is simulators. In this paper a powertrain model and a procedure for parameterizing it, using chassis dynamometers and a developed pedal robot are presented. The parameterizing procedure uses the on-board diagnostics of the car and does not require any additional invasive sensors.
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.
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 nonlinear four state-three input mean value engine model (MVEM), incorporating the important turbocharger dynamics, is used to study optimal control of a diesel-electric powertrain during transients. The optimization is conducted for the two criteria, minimum time and fuel, where both engine speed and engine power are considered free variables in the optimization. First, steps from idle to a target power are studied and for steps to higher powers the controls for both criteria follow a similar structure, dictated by the maximum torque line and the smoke-limiter. The end operating point, and how it is approached is, however, different. Then, the power transients are extended to driving missions, defined as, that a certain power has to be met as well as a certain energy has to be produced. This is done both with fixed output profiles and with the output power being a free variable. The time optimal control follows the fixed output profile even when the output power is free. These solutions are found to be almost fuel optimal despite being substantially faster than the minimum fuel solution with variable output power. The discussed control strategies are also seen to hold for sequences of power and energy steps.
The effects of generator model and energy storage on the optimal control of a diesel-electric powertrain in transient operation are studied. Two different types of problems are solved, minimum fuel and minimum time, with different generator models and limits as well as with an extra energy storage. For this aim, a four-state mean value engine model (MVEM) is used together with models for the generator and energy storage losses. In the optimization both the engines output power and speed are free variables. The considered transients are steps from idle to target power with different amounts of freedom, defined as requirements on produced energy, before the requested power has to be met. The main characteristics are seen to be independent of generator model and limits; they, however, shift the peak efficiency regions and therefore the stationary points. For minimum fuel transients, the energy storage remains virtually unused for all requested energies, for minimum time it is used to reduce the response time. The generator limits are found to have the biggest impact on the fuel economy, whereas an energy storage could significantly reduce the response time. The possibility to reduce the response time is seen to hold for a large range of values of energy storage parameters. The minimum fuel solutions remain unaffected when changing the energy storage parameters, implying it is not beneficial to use an energy storage if fuel consumption is to be minimized. Close to the minimum time solution, the fuel consumption with low required energy is quite sensitive to variations in duration, for larger energies it is not. Near the minimum fuel solution changes in duration have only minor effects on the fuel consumption.
The importance of including turbocharger dynamics in diesel engine models are studied, especially when optimization techniques are to be used to derive the optimal controls. This is done for two applications of diesel engines where in the first application, a diesel engine in wheel loader powertrain interacts with other subsystems to perform a loading operation and engine speed is dictated by the wheel speed, while in the second application, the engine operates in a diesel-electric powertrain as a separate system and the engine speed remains a free variable. In both applications, mean value engine models of different complexities are used while the rest of system components are modeled with the aim of control study. Optimal control problems are formulated, solved, and results are analyzed for various engine loading scenarios in the two applications with and without turbocharger dynamics. It is shown that depending on the engine loading transients, fuel consumption and operation time can widely vary when the turbocharger dynamics are considered in the diesel engine model. Including these, have minor effects on fuel consumption and operation time at minimum fuel operations of the first application (~0.1 %) while the changes are considerable in the second application (up to 60%). In case of minimum time operations however, fuel consumption and operation time are highly affected in both applications implying that not considering turbocharger dynamics in the diesel engine models may lead to overestimation of the engine performance especially when the results are going to be used for control purposes.
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. 2b1af7f3a8