Chalmers University of Technology

About: Chalmers University of Technology is a education organization based out in Gothenburg, Sweden. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 17191 authors who have published 53951 publications receiving 1520592 citations. The organization is also known as: Chalmers Tekniska Högskola & Chalmers.

Fast charging infrastructure for electric vehicles: Today’s situation and future needs

Abstract: Potential users of plug-in electric vehicles often ask for public charging facilities before buying vehicles. Furthermore, the speed of public charging is often expected to be similar to conventional refueling. For this reason, research on and political interest in public charging focus more and more on fast charging options with higher power rates, yet estimates for future needs are rare. This paper tries to fill this gap by analyzing current charging behavior from a large charging data set from Sweden and Norway and take the findings to calibrate a queuing model for future fast charging infrastructure needs. We find that the ratio of battery electric vehicles to public fast charging points can be similar to other alternative fuels in the future (close to one fast charging point per 1000 vehicles for high power rates of 150 kW). In addition, the surplus on the electricity prices for payoff is only 0.05–0.15 €/kWh per charging point. However, charging infrastructure needs highly depend on battery sizes and power rates that are both likely to increase in the future.

Cooperative Received Signal Strength-Based Sensor Localization With Unknown Transmit Powers

Abstract: Cooperative localization (also known as sensor network localization) using received signal strength (RSS) measurements when the source transmit powers are different and unknown is investigated. Previous studies were based on the assumption that the transmit powers of source nodes are the same and perfectly known which is not practical. In this paper, the source transmit powers are considered as nuisance parameters and estimated along with the source locations. The corresponding Cramer-Rao lower bound (CRLB) of the problem is derived. To find the maximum likelihood (ML) estimator, it is necessary to solve a nonlinear and nonconvex optimization problem, which is computationally complex. To avoid the difficulty in solving the ML estimator, we derive a novel semidefinite programming (SDP) relaxation technique by converting the ML minimization problem into a convex problem which can be solved efficiently. The algorithm requires only an estimate of the path loss exponent (PLE). We initially assume that perfect knowledge of the PLE is available, but we then examine the effect of imperfect knowledge of the PLE on the proposed SDP algorithm. The complexity analyses of the proposed algorithms are also studied in detail. Computer simulations showing the remarkable performance of the proposed SDP algorithm are presented.

The uptake of the ecosystem services concept in planning discourses of European and American cities

Abstract: Ecosystem services (ES) are gaining increasing attention as a promising concept to more actively consider and plan for the varied benefits of the urban environment. Yet, to have an impact on decision-making, the concept must spread from academia to practice. To understand how ES have been taken up in planning discourses we conducted a cross-case comparison of planning documents in Berlin, New York, Salzburg, Seattle and Stockholm. We found: (1) explicit references to the ES concept were primarily in documents from Stockholm and New York, two cities in countries that entered into ES discourses early. (2) Implicit references and thus potential linkages between the ES concept and planning discourses were found frequently among all cities, especially in Seattle. (3) The thematic scope, represented by 21 different ES, is comparably broad among the cases, while cultural services and habitat provision are most frequently emphasized. (4) High-level policies were shown to promote the adoption of the ES concept in planning. We find that the ES concept holds potential to strengthen a holistic consideration of urban nature and its benefits in planning. We also revealed potential for further development of ES approaches with regard to mitigation of environmental impacts and improving urban resilience.

Spatial modeling with R-INLA: A review

Abstract: Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Matern Gaussian random fields. In this review, we discuss the large success of spatial modeling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for nonstationary spatial models and nonseparable space–time models. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Bayesian Models Data: Types and Structure > Massive Data.