Sampled-Data and Harmonic Balance Analyses of Average Current-Mode Controlled Buck Converter

Dynamics and stability of average current-mode control of buck converters are analyzed by sampled-data and harmonic balance analyses. An exact sampled-data model is derived. A new continuous-time model "lifted" from the sampled-data model is also derived, and has frequency response matched with experimental data reported previously. Orbital stability is studied and it is found unrelated to the ripple size of the current-loop compensator output. An unstable window of the current-loop compensator pole is found by simulations, and it can be accurately predicted by sampled-data and harmonic balance analyses. A new S plot accurately predicting the subharmonic oscillation is proposed. The S plot assists pole assignment and shows the required ramp slope to avoid instability.Comment: Submitted to International Journal of Circuit Theory and Applications on August 9, 2011; Manuscript ID: CTA-11-016

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries

Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies