Plug-In Electric Vehicles

Did you know?Using electricity to power an automotive vehicle would cost the equivalent of paying less than $2.00 per gallon of gasoline at current electric prices. In some states, the cost would be under$1.00 per gallon.A typical mid-size sedan, when running on electricity from the current U.S. grid, would have the same carbon footprint as a car that gets 50 miles per gallon (mpg) of gasoline. As more electricity comes from renewable sources, net carbon emissions would be reduced further. Plug-in electric vehicles could be on the market very soon

Plug in to grid computing

This article discusses the potential benefits of grid computing for future power networks. It is also intended to alert the power system community to the concept of grid computing and to initiate a discussion of its potential applications in future power systems. Much like the Web, the grid can operate over the Internet or any other suitable computer networking technology. Grid computing offers an inexpensive and efficient means for participants to compete (but also cooperate) in providing reliable, cheap, and sustainable electrical energy supply. It also provides a relatively inexpensive new technology allowing the output of embedded generators to be monitored and, when necessary, controlled. Basically, the ability of grid-enabled systems to interact autonomously is vital for small generators where manned operation is likely to be viable

An Architecture For Creating Hosting Plug- Ins For Use In Digital Audio Workstations

Although modern software-based DA Ws (Digital Audio Workstations) offer the ability to interconnect with plug-in effects, they can be restrictive due to their architecture being largely based on hardware mixing desks. This is especially true when complex multi-effect sound design is required. This paper aims to demonstrate how a plug-in that can host other effects plug-ins can help improve the sound design possibilities in a DAW. This hosting plug-in allows other effects to be “inserted” at specific points in its internal signal flow. Details are given of a “proof of concept” plug-in that was created to demonstrate that it was possible to create plug-ins that can host other plug-ins, using Apple’s AU (Audio Unit) format. The proof of concept is a delay effect that allows other effects plug-ins to be inserted in either the “delay path”, “feedback path” or both. This Audio Unit has been extensively tested using different DAWs and has been found to work successfully in a variety of situations. Finally, details are given of how improvements can be made to the plug-in hosting delay

Identifiability issues of age-period and age-period-cohort models of the Lee-Carter type

The predominant way of modelling mortality rates is the Lee-Carter model and its many extensions. The Lee-Carter model and its many extensions use a latent process to forecast. These models are estimated using a two-step procedure that causes an inconsistent view on the latent variable. This paper considers identifiability issues of these models from a perspective that acknowledges the latent variable as a stochastic process from the beginning. We call this perspective the plug-in age-period or plug-in age-period-cohort model. Defining a parameter vector that includes the underlying parameters of this process rather than its realisations, we investigate whether the expected values and covariances of the plug-in Lee-Carter models are identifiable. It will be seen, for example, that even if in both steps of the estimation procedure we have identifiability in a certain sense it does not necessarily carry over to the plug-in models

Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters

The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location