This article presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is a classical model for the self-synchronization of coupled oscillators. The mapping between phase and location for vehicles on different roads is designed such that Kuramoto synchronization ensures safe intersection navigation. Each vehicle uses a constrained optimal control policy to achieve its desired target Kuramoto phase at the upcoming intersection. The overall outcome is a distributed traffic synchronization algorithm that simultaneously tackles two challenges traditionally addressed independently, namely: coordinating crossing at an individual intersection, and harmonizing traffic flow between adjacent intersections. Simulation studies highlight the positive impact of this strategy on fuel consumption and traffic delay time, compared to a network with traditional traffic light timing.

This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.

Energy storage - in the form of UPS units - in a datacenter has been primarily used to fail-over to diesel generators upon power outages. There has been recent interest in using these Energy Storage Devices (ESDs) for demand-response (DR) to either shift peak demand away from high tariff periods, or to shave demand allowing aggressive under-provisioning of the power infrastructure. All such prior work has only considered a single/specific type of ESD (typically re-chargeable lead-acid batteries), and has only employed them at a single level of the power delivery network. Continuing technological advances have provided us a plethora of competitive ESD options ranging from ultra-capacitors, to different kinds of batteries, flywheels and even compressed air-based storage. These ESDs offer very different trade-offs between their power and energy costs, densities, lifetimes, and energy efficiency, among other factors, suggesting that employing hybrid combinations of these may allow more effective DR than with a single technology. Furthermore, ESDs can be placed at different, and possibly multiple, levels of the power delivery hierarchy with different associated trade-offs. To our knowledge, no prior work has studied the extensive design space involving multiple ESD technology provisioning and placement options. This paper intends to fill this critical void, by presenting a theoretical framework for capturing important characteristics of different ESD technologies, the trade-offs of placing them at different levels of the power hierarchy, and quantifying the resulting cost-benefit trade-offs as a function of workload properties.

It is well established in the literature that secondary tasks adversely affect driving behavior. Previous research has focused on discovering the general trends by analyzing the average effects of secondary tasks on a population of drivers. This paper conjectures that there may also be individual effects, i.e., different effects of secondary tasks on individual drivers, which may be obscured within the average behavior of the population, and proposes a model-based approach to analyze them. Specifically, a radial-basis neural-network-based modeling framework is developed to characterize the normal driving behavior of a driver when driving without secondary tasks. The model is then used in a scenario of driving with a secondary task to predict the hypothetical actions of the driver, had there been no secondary tasks. The difference between the predicted normal behavior and the actual distracted behavior gives individual insight into how the secondary tasks affect the driver. It is shown that this framework can help uncover the different effects of secondary tasks on each driver, and when used together with support vector machines, it can help systematically classify normal and distracted driving conditions for each driver.

Access links are typically the bottleneck between a high bandwidth LAN and a high bandwidth IP network. Without a priori resource provisioning or reservation, this tends to have a serious effect on premium traffic from delay- and jitter-sensitive applications. This paper develops a new reservation-less mechanism for delay and jitter guarantees across "single FIFO queue" access links by controlling interfering non-premium traffic. The mechanism, called CONNET (CONtrolled NETwork), uses feedback regulation to create self-controlled network services capable of tracking delay and jitter references. We use a control-theoretic approach in designing the feedback loop based on a "black-box" model of the access link in both cases of single and multiple nodes. For robust control, we employ optimal Linear Quadratic Gaussian (LQG) regulator. We evaluate the performance of CONNET using a real testbed implementation showing its superiority to other rate-based schemes (such as CBQ and WFQ) in terms of simplicity, deployability, and accuracy.