Vera Pantelic

Making the Case for Electrified Transportation

In order to achieve lower fuel consumption and less greenhouse gas (GHG) emissions, we need higher efficiency vehicles with improved performance. Electrification is the most promising solution to enable a more sustainable and environmentally friendly transportation system. Electrified transportation vision includes utilizing more electrical energy to power traction and nontraction loads in the vehicle. In electrified powertrain applications, the efficiency of the electrical path, and the power and energy density of the components play important roles to improve the electric range of the vehicle to run the engine close to its peak efficiency point and to maintain lower energy consumption with less emissions. In general, the electrified powertrain architecture, design and control of the powertrain components, and software development are coupled to facilitate an efficient, high-performance, and reliable powertrain. In this paper, enabling technologies and solutions for the electrified transportation are discussed in terms of power electronics, electric machines, electrified powertrain architectures, energy storage systems (ESSs), and controls and software.

Probabilistic Supervisory Control of Probabilistic Discrete Event Systems

This paper considers supervisory control of probabilistic discrete event systems (PDESs). PDESs are modeled as generators of probabilistic languages. The supervisory control problem considered is to find, if possible, a supervisor under whose control the behavior of a plant is identical to a given probabilistic specification. The probabilistic supervisors we employ are a generalization of the deterministic ones previously employed in the literature. At any state, the supervisor enables/disables events with certain probabilities. Necessary and sufficient conditions for the existence of such a supervisor, and an algorithm for its computation are presented.

Optimal Supervisory Control of Probabilistic Discrete Event Systems

Probabilistic discrete event systems (PDES) are modeled as generators of probabilistic languages and the supervisors employed are a probabilistic generalization of deterministic supervisors used in standard supervisory control theory. In the case when there exists no probabilistic supervisor such that the behavior of a plant under control exactly matches the probabilistic language given as the requirements specification, we want to find a probabilistic control such that the behavior of the plant under control is “as close as possible” to the desired behavior. First, as a measure of this proximity, a pseudometric on states of generators is defined. Two algorithms for the calculation of the distance between states in this pseudometric are described. Then, an algorithm to synthesize a probabilistic supervisor that minimizes the distance between generators representing the achievable and required behavior of the plant is presented.

A toolset for Simulink: Improving software engineering practices in development with Simulink

This paper presents a set of tools that provide automatic support for application of some of the traditional software engineering practices when developing with Simulink. The tools are the: Signature Tool, Reach/Coreach Tool, Data Store Push-Down Tool, and Auto Layout Tool. The Signature Tool extracts the interface of a Simulink subsystem, identifying the subsystems explicit, and implicit data flow mechanisms, empowering developers to use the implicit mechanisms more effectively. The Reach/Coreach Tool identifies data and control flow dependencies in a Simulink model and uses the information for model slicing. The view of dependencies offered by the tool significantly eases the comprehension of large models. The dependencies can also serve as indicators of alternative designs, and facilitate more effective testing and verification. The Data Store Push-Down Tool restricts the scope of Simulinks data stores thereby providing improved encapsulation, and increasing modularity. Finally, the Auto Layout Tool significantly decreases the manual effort developers spend in achieving proper layout of models during design and refactoring, and can be used by automated refactoring and transformation tools.

Towards Optimal Supervisory Control of Probabilistic Discrete Event Systems

This paper considers optimal supervisory control of probabilistic discrete event systems (PDESs). PDESs are modeled as generators of probabilistic languages. The probabilistic supervisors employed enable/disable events with certain probabilities. We consider the case when there exists no probabilistic supervisor to match the behaviour of a plant to a probabilistic requirements specification. First, we define a notion of distance between two probabilistic generators. Then, given a plant and a desired probabilistic behaviour, we present an algorithm that minimizes the distance between the desired behaviour and the behaviour of the controlled plant achievable under probabilistic control.

Signature required

Model comprehension and effective use and reuse of complex subsystems are problems currently encountered in the automotive industry. To address these problems we present a technique for extracting, presenting and making use of signatures for Simulink subsystems. The signature of a subsystem is defined to be a generalization of its interface, including the subsystems explicit ports, locally defined and inherited data stores, and scoped gotos/froms. We argue that the use of signatures has significant benefits for model comprehension and subsystem testing, and show how the incorporation of signatures into existing Simulink models is practical and useful by discussing various usage scenarios.

Software engineering practices and Simulink: bridging the gap

Although widely used in embedded systems design, Matlab/Simulink is not considered a state-of-the-art design environment by the software engineering community. This paper is aimed at improving design with Simulink from the software engineering perspective by developing automated support for the application of some traditional software engineering principles when developing with Simulink. We present four tools: the Signature Tool, the Reach/Coreach Tool, the Data Store Rescope Tool, and the Auto Layout Tool. The Signature Tool extracts the interface of a Simulink subsystem, enabling developers to better understand the implicit data flow in Simulink models and use it more effectively, while also producing useful documentation. The Data Store Rescope Tool improves modularity of Simulink models by properly scoping data stores, the Simulink equivalent of variables in traditional languages. The Reach/Coreach Tool highlights data and control dependencies in Simulink models, making them easier to understand. Also, the tool supports debugging, reverse-engineering, refactoring, and static analysis of the models. Finally, the Auto Layout Tool automatically improves the layout of Simulink models, reducing the effort developers invest in graphical layout to comply with modeling guidelines and improve readability of their models.

Using STPA in an ISO 26262 compliant process

ISO 26262 is the de facto standard for automotive functional safety, and every automotive Original Equipment Manufacturer (OEM), as well as their major suppliers, are striving to ensure that their development processes are ISO 26262 compliant. ISO 26262 mandates both hazard analysis and risk assessment. Systems Theoretic Process Analysis (STPA) is a relatively new hazard analysis technique, that promises to overcome some limitations of traditional hazard analysis techniques. In this paper, we analyze how STPA can be used in an ISO 26262 compliant process. We also provide an excerpt of our application of STPA on an automotive subsystem as per the concept phase of ISO 26262.

Use of a Metric in Supervisory Control of Probabilistic Discrete Event Systems

Abstract This work represents a natural extension of our work on optimal probabilistic supervisory control of probabilistic discrete event systems (PDESs). In that work, a pseudometric on the initial states of two probabilistic generators that represent probabilistic systems is used to measure the distance between the two systems. The pseudometric is given a fixed point characterization. This paper gives a logical characterization of the same pseudometric such that the distance between two systems is measured by a formula that distinguishes between the systems the most. A trace characterization of the pseudometric is then derived from the logical characterization. Further, the solution of the problem of approximation of a given probabilistic generator with another generator of a prespecified structure is suggested such that the new model is as close as possible to the original one in the pseudometric. The significance of the approximation is then discussed.

A Framework for Supervisory Control of Probabilistic Discrete Event Systems

Abstract This paper focuses on a framework for probabilistic supervisory control of probabilistic discrete event systems (PDES). PDES are modelled as generators of probabilistic languages, and the supervisors used are probabilistic. In our previous work, we presented and solved a number of supervisory control problems inside the framework. We also suggested a pseudometric to measure the behavioural similarity between PDES, and used the pseudometric in the solution of two optimal supervisory control problems defined in the framework. In this paper, we survey these results and introduce a real-world application of the framework. Further, we investigate a relationship between our framework and that of Markov Decision Processes, that could prove beneficial for both control synthesis and probabilistic model checking.