For years the trend in the automotive industry has been toward more complex electronic control systems. The number of electronic control units (ECUs) in vehicles is ever increasing as is the complexity of communication networks among the ECUs. Increasing fuel economy standards and the increasing cost of fuel is driving hybridization and electrification of the automobile. Achieving superior fuel economy with a hybrid powertrain requires an effective and optimized control system. On the other hand, mathematical modeling and simulation tools have become extremely advanced and have turned simulation into a powerful design tool. The combination of increasing control system complexity and simulation technology has led to an industry wide trend toward model based control design. Rather than using models to analyze and validate real world testing data, simulation is now the primary tool used in the design process long before real world testing is possible. Modeling is used in every step from architecture selection to control system validation before on-road testing begins. The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the 2011-2014 EcoCAR 2 competition in which the team is tasked with re-engineering the powertrain of a GM donated vehicle. The primary goals of the competition are to reduce well to wheels (WTW) petroleum energy use (PEU) and reduce WTW greenhouse gas (GHG) and criteria emissions while maintaining performance, safety, and consumer acceptability. This paper will present systematic methodology for using model based design techniques for architecture selection, control system design, control strategy optimization, and controller validation to meet the goals of the competition. Simple energy management and efficiency analysis will form the primary basis of architecture selection. Using a novel method, a series-parallel powertrain architecture is selected. The control system architecture and requirements is defined using a systematic approach based around the interactions between control units. Vehicle communication networks are designed to facilitate efficient data flow. Software-in-the-loop (SIL) simulation with Mathworks Simulink is used to refine a control strategy to maximize fuel economy. Finally hardware-in-the-loop (HIL) testing on a dSPACE HIL simulator is demonstrated for performance improvements, as well as for safety critical controller validation. The end product of this design study is a control system that has reached a high level of parameter optimization and validation ready for on-road testing in a vehicle. / Master of Science

One of the largest energy losses in an excavator is the compensation loss. In a hydraulic load sensing system where one pump supplies multiple actuators, these compensation losses are inevitable. To minimize the compensation losses the use of a multi chamber cylinder can be used, which can control the load pressure by activate its chambers in different combinations and in turn minimize the compensation losses.  For this proposed architecture, the control of the multi chamber cylinder systems is not trivial. The possible states of the system, due to the number of combinations, makes conventional control, like a rule based strategy, unfeasible. Therefore, is the reinforcement learning a promising approach to find an optimal control.  A hydraulic system was modeled and validated against a physical one, as a base for the reinforcement learning to learn in simulation environment. A satisfactory model was achieved, accurately modeled the static behavior of the system but lacks some dynamics.  A Deep Q-Network agent was used which successfully managed to select optimal combinations for given loads when implemented in the physical test rig, even though the simulation model was not perfect.

Scenario-based testing will help to validate automated driving systems (ADS) and establish safer road traffic. To date, most data-driven test scenario generation methods rely primarily on one data source such as police accident data (PD), naturalistic driving studies, or video-based traffic observations (VOs). However, none of these data sources perfectly satisfies all the layers of the six-layer model for the description of test scenarios. Moreover, not all available data sources cover the same location and period of time. Therefore, we fused information from 1,648 scenarios extracted from a German VO with information from 74 scenarios extracted from German PD into a comprehensive new PD* database. Finally, PD* consisted of 74 accident scenarios extended, for example, by variables containing the dynamic information of the VO scenarios. Thus, PD* contained more than 350 variables, whereas PD contained only 269 and VO only 122 variables. For fusion, we followed the Find-Unify-Synthesize-Evaluation (FUSE) for Representativity (FUSE4Rep) process model using statistical matching. Subsequently, we derived three logical scenarios from PD* to test an autonomous emergency braking system (AEB) in a stochastic traffic simulation incorporating driver-behavior models. The quality of the fusion itself was satisfactory, and we propose improving the VO data collection process and observation time to obtain even better results.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2012-2014 EcoCAR 2: Plugging in to the Future Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM) and the U.S. Department of Energy (DOE). The goals of the competition are to reduce well-to-wheel (WTW) petroleum energy consumption (PEU), WTW greenhouse gas (GHG) and criteria emissions while maintaining vehicle performance, consumer acceptability and safety. Following the EcoCAR 2 Vehicle Development Process (VDP) of designing, building, and refining an advanced technology vehicle over the course of the three year competition using a 2013 Chevrolet Malibu donated by GM as a base vehicle, the selected powertrain is a Series-Parallel Plug-In Hybrid Electric Vehicle (PHEV) with P2 (between engine and transmission) and P4 (rear axle) motors, a lithium-ion battery pack, an internal combustion engine, and an automatic transmission. Development of a charge sustaining control strategy for this vehicle involves coordination of controls for each of the main powertrain components through a distributed control strategy. This distributed control strategy includes component controllers for each individual component and a single supervisory controller responsible for interpreting driver demand and determining component commands to meet the driver demand safely and efficiently. For example, the algorithm accounts for a variety of system operating points and will penalize or reward certain operating points for other conditions. These conditions include but are not limited to rewards for discharging the battery when the state of charge (SOC) is above the target value or penalties for operating points with excessive emissions. Development of diagnostics and remedial actions is an important part of controlling the powertrain safely. In order to validate the control strategy prior to in-vehicle operation, simulations are run against a plant model of the vehicle systems. This plant model can be run in both controller Software- and controller Hardware-In-the-Loop (SIL and HIL) simulations. This paper details the development of the controls for diagnostics, major selection algorithms, and execution of commands and its integration into the Series-Parallel PHEV through the supervisory controller. This paper also covers the plant model development and testing of the control algorithms using controller SIL and HIL methods. This paper details reasons for any changes to the control system, and describes improvements or tradeoffs that had to be made to the control system architecture for the vehicle to run reliably and meet its target specifications. Test results illustrate how changes to the plant model and control code properly affect operation of the control system in the actual vehicle. The VT Malibu is operational and projected to perform well at the final competition. / Master of Science

Afin de garantir qu'un programme respectera toutes ses contraintes temporelles, nous devons être capable de calculer une estimation fiable de son temps d'exécution au pire cas (WCET: worst case execution time). Cependant, identifier une borne précise du pire temps d'exécution devient une tâche très complexe du fait de la sophistication croissante des processeurs. Ainsi, l'objectif de nos travaux de recherche a été de définir une méthode formelle qui puisse s'adapter aux évolutions du matériel. Cette méthode consiste à développer un modèle du processeur cible, puis à l'exécuter symboliquement afin d'associer à chaque trace d'exécution un temps d'exécution au pire cas. Une méthode de fusionnement est également prévue afin d'éviter une possible explosion combinatoire. Cette méthode a pour principale contrainte de ne pas introduire trop d'imprécision sur les temps calculés. / To ensure that a program will respect all its timing constraints we must be able to compute a safe estimation of its worst case execution time (WCET). However with the increasing sophistication of the processors, computing a precise estimation of the WCET becomes very difficult. In this report, we propose a novel formal method to compute a precise estimation of the WCET that can be easily parameterized by the hardware architecture. Assuming that we developed an executable timed model of the hardware, we use symbolic execution to precisely infer the execution time for a given instruction flow. We also merge the states relying on the loss of precision we are ready to accept, in order to avoid a possible states explosion.

<p dir="ltr">The rise of battery electric vehicles (BEVs) has been driven by global initiatives to reduce carbon emissions and support technological advancements in battery technology. However, heat loads in these vehicles are inherently transient, and traditional thermal management system (TMS) design approaches are not suitable for designing TMS that allow up-front consideration of transient operation. Graph-based modeling has been explored as a tool for modeling dynamic systems, including thermal systems, due to its modularity and suitability for control design and optimization. It has been successfully applied to air-cycle machines and component-level thermal modeling. For BEV applications, there is an opportunity to expand graph-based modeling into system-level TMS modeling. This approach can solve the complexities of the BEV thermal management, especially with the needs of the rapidly evolving automotive industry.</p><p dir="ltr"> In this thesis, I present the modeling of a BEV TMS using a graph-based modeling framework at both the component and cycle levels. By developing a physics-based, reduced-order model, the thermal interactions within individual components and between connected components are analyzed and discussed in detail. Furthermore, I validate the graph-based model against a high-fidelity benchmark model to assess its accuracy and reliability. The validation process involves simulating and analyzing the dynamic state variables and key performance parameters of the TMS, including temperature, pressure, and enthalpy. These metrics are compared to a high-fidelity benchmark model across various operating conditions. The validated framework provides a strong foundation for future advancements in thermal management systems for BEVs.</p>