This project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level. Charging at both commercial charging stations and at residences will be considered, for both the case when PEVs only function as loads, and the case when they can also function as sources, equipped with vehicle-to-home (V2H) or vehicle-to-grid (V2G) energy reinjection capability. The focus of the project is on distributed decision making by various individual players to achieve analytical system-level performance guarantees. Electrification of the transportation market offers revenue growth for utility companies and automobile manufacturers, lower operational costs for consumers, and benefits to the environment. By addressing problems that will arise as PEVs impose extra load on the grid, and by solving challenges that currently impede the use of PEVs as distributed storage resources, this research will directly impact the society. The design principles gained will also be applicable to other cyber-physical infrastructural systems. A close collaboration with industrial partners will ground the research in real problems and ensure quick dissemination of results to the marketplace. A strong educational component will integrate the proposed research into the classroom to allow better training of both undergraduate and graduate students. The details of the project will be provided at http://ee.nd.edu/faculty/vgupta/research/funding/cps_pev.html

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

This research aims at hybrid (discrete-continuous) computation for cyber-physical systems. The research augments the today-ubiquitous discrete (digital) model of computation with continuous (analog) computing, which is well-suited to the continuous natural variables involved in cyber-physical systems, and to the error-tolerant nature of computation in such systems. The result is a computing platform on a single silicon chip, with higher energy efficiency, higher speed, and better numerical convergence than is possible with purely discrete computation. The research has several thrusts: (1) Hardware: modern silicon chip technology is used to merge analog computing hardware on the same chip with digital hardware, the latter used for control and co-computation, (2) Architecture: methods are devised for making hybrid computing functionality accessible to the software, (3) Microarchitecture: Choices are made on the granularity, type and organization of analog and hybrid analog-digital functional units, and (4) Concrete application to a realistic cyber-physical system consisting of a team of robots. The research extends modern computer architecture techniques, and advances in mixed analog/digital chip technology mainly developed in the context of communications, to hybrid computing for cyber-physical systems. It brings higher levels of energy efficiency to error-tolerant workloads that future computers will have to handle. The techniques developed can be extended to other systems in which efficient computation is a must, such as weather forecasting and high-energy physics. The work integrates research with education and includes plans for broad dissemination of the results obtained.

This research aims at hybrid (discrete-continuous) computation for cyber-physical systems. The research augments the today-ubiquitous discrete (digital) model of computation with continuous (analog) computing, which is well-suited to the continuous natural variables involved in cyber-physical systems, and to the error-tolerant nature of computation in such systems. The result is a computing platform on a single silicon chip, with higher energy efficiency, higher speed, and better numerical convergence than is possible with purely discrete computation. The research has several thrusts: (1) Hardware: modern silicon chip technology is used to merge analog computing hardware on the same chip with digital hardware, the latter used for control and co-computation, (2) Architecture: methods are devised for making hybrid computing functionality accessible to the software, (3) Microarchitecture: Choices are made on the granularity, type and organization of analog and hybrid analog-digital functional units, and (4) Concrete application to a realistic cyber-physical system consisting of a team of robots. The research extends modern computer architecture techniques, and advances in mixed analog/digital chip technology mainly developed in the context of communications, to hybrid computing for cyber-physical systems. It brings higher levels of energy efficiency to error-tolerant workloads that future computers will have to handle. The techniques developed can be extended to other systems in which efficient computation is a must, such as weather forecasting and high-energy physics. The work integrates research with education and includes plans for broad dissemination of the results obtained.

The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level. Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced. Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.