Least costly energy management for series hybrid electric vehicles

Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign

Hidden scaling patterns and universality in written communication

The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's `proper time' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near -3/2. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bi-modal character, which is empirically borne out by our new years-long data on email. These novel perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, emergency handling.Comment: 27 pages, 10 figure

Neutral dynamics with environmental noise: age-size statistics and species lifetimes

Neutral dynamics, where taxa are assumed to be demographically equivalent and their abundance is governed solely by the stochasticity of the underlying birth-death process, has proved itself as an important minimal model that accounts for many empirical datasets in genetics and ecology. However, the restriction of the model to demographic [${\cal{O}} ({\sqrt N})$] noise yields relatively slow dynamics that appears to be in conflict with both short-term and long-term characteristics of the observed systems. Here we analyze two of these problems - age size relationships and species extinction time - in the framework of a neutral theory with both demographic and environmental stochasticity. It turns out that environmentally induced variations of the demographic rates control the long-term dynamics and modify dramatically the predictions of the neutral theory with demographic noise only, yielding much better agreement with empirical data. We consider two prototypes of "zero mean" environmental noise, one which is balanced with regard to the arithmetic abundance, another balanced in the logarithmic (fitness) space, study their species lifetime statistics and discuss their relevance to realistic models of community dynamics

Direct learning ofLPVcontrollers from data

In many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV) plants by linear parametervarying (LPV) models and design controllers based on such representations to regulate the behaviour of the system. The LPV system class offers the representation of NL and TV phenomena as a linear dynamic relationship between input and output signals, which relationship is dependent on some measurable signals, e.g., operating conditions, often called as scheduling variables. For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research. Therefore, it is attractive to synthesize the controller directly from data without the need of modeling the plant and addressing the underlying difficulties. Hence, in this paper, a novel data-driven synthesis scheme is proposed in a stochastic framework to provide a practically applicable solution for synthesizing LPV controllers directly from data. Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided. The effectiveness of the proposed methods is also illustrated by means of an academic example and a real application based simulation case study

Zoo-technical application of Ground Source Heat Pumps: a pilot case study

Ground Source Heat Pumps are energy-efficient HVAC systems usually adopted in residential and commercial buildings. However the control of the thermal environment is required not only in spaces occupied by people, but also in intensive breeding farms, in order to maintain healthy conditions and to increase productivity. In the Italian livestock breedings, heating is usually provided by means of gas or Diesel burners directly installed in the stable. An important part of the heating load is due to the large ventilation rates required for the livestock wellbeing. Cooling is either absent or achieved by evaporative systems that also increase the humidity level in the stables, thus requiring even larger ventilation rates. Therefore the applicability of geothermal heating and cooling in breeding farms was analysed in a research project co-funded by the Lombardy Region and the Italian Ministry of Research and Education. A pilot system for heating, cooling and ventilation was designed and installed in a piglets room at the Experimental and Didactic Zoo-technical Center of the University of Milan. Five Borehole Heat Exchangers (BHEs), installed down to a depth of 60 meters into an alluvial aquifer, were coupled with a Ground Source Heat Pump. The heat pump provides heating and cooling to an Air Handling Unit, including a Heat Recovery system. A monitoring system was installed in order to measure comfort conditions in the piglet room, operating conditions and energy consumption of the HVAC system, together with the spreading of the thermal plume in the ground. In this paper the results of a monitoring campaign carried out in a typical winter period are presented and discussed. The overall energy efficiency of the system, expressed in terms of a COP, results to be equal to 4.04. A comparison between the pilot HVAC system and a traditional one is also carried out, showing that the proposed solution can provide over 40% primary energy saving. Following, cost savings in energy bills for farmers are found, although the ratio between electricity cost and fuel cost is a key parameter

Direct data-driven control of linear parameter-varying systems

In many control applications, nonlinear plants can be modeled as linear parameter-varying (LPV) systems, by which the dynamic behavior is assumed to be linear, but also dependent on some measurable signals, e.g., operating conditions. When a measured data set is available, LPV model identification can provide low complexity linear models that can embed the underlying nonlinear dynamic behavior of the plant. For such models, powerful control synthesis tools are available, but the way the modeling error and the conservativeness of the embedding affect the control performance is still largely unknown. Therefore, it appears to be attractive to directly synthesize the controller from data without modeling the plant. In this paper, a novel data-driven synthesis scheme is proposed to lay the basic foundations of future research on this challenging problem. The effectiveness of the proposed approach is illustrated by a numerical example

Metastates in mean-field models with random external fields generated by Markov chains

We extend the construction by Kuelske and Iacobelli of metastates in finite-state mean-field models in independent disorder to situations where the local disorder terms are are a sample of an external ergodic Markov chain in equilibrium. We show that for non-degenerate Markov chains, the structure of the theorems is analogous to the case of i.i.d. variables when the limiting weights in the metastate are expressed with the aid of a CLT for the occupation time measure of the chain. As a new phenomenon we also show in a Potts example that, for a degenerate non-reversible chain this CLT approximation is not enough and the metastate can have less symmetry than the symmetry of the interaction and a Gaussian approximation of disorder fluctuations would suggest.Comment: 20 pages, 2 figure

A symmetric entropy bound on the non-reconstruction regime of Markov chains on Galton-Watson trees

We give a criterion of the form Q(d)c(M)<1 for the non-reconstructability of tree-indexed q-state Markov chains obtained by broadcasting a signal from the root with a given transition matrix M. Here c(M) is an explicit function, which is convex over the set of M's with a given invariant distribution, that is defined in terms of a (q-1)-dimensional variational problem over symmetric entropies. Further Q(d) is the expected number of offspring on the Galton-Watson tree. This result is equivalent to proving the extremality of the free boundary condition-Gibbs measure within the corresponding Gibbs-simplex. Our theorem holds for possibly non-reversible M and its proof is based on a general Recursion Formula for expectations of a symmetrized relative entropy function, which invites their use as a Lyapunov function. In the case of the Potts model, the present theorem reproduces earlier results of the authors, with a simplified proof, in the case of the symmetric Ising model (where the argument becomes similar to the approach of Pemantle and Peres) the method produces the correct reconstruction threshold), in the case of the (strongly) asymmetric Ising model where the Kesten-Stigum bound is known to be not sharp the method provides improved numerical bounds.Comment: 10 page

Statistical Analysis of the Wave Runup at Walls in a Changing Climate by Means of Image Clustering

This contribution builds on an existing methodology of image clustering analysis, conceived for modelling the wave overtopping at dikes from video records of laboratory experiments. It presents new procedures and algorithms developed to extend this methodology to the representation of the wave runup at crown walls on top of smooth berms. The upgraded methodology overcomes the perspective distortion of the native images and deals with the unsteady, turbulent and bi-phase flow dynamics characterizing the wave impacts at the walls. It accurately reconstructs the free surface along the whole structure profile and allows for a statistical analysis of the wave runup in the time and spatial domain. The effects of different structural configurations are investigated to provide key information for the design of coastal defences. In particular, the effects of increased sea levels in climate change scenarios are analysed. Innovative results, such as profiling of the envelopes of the runup along the wall cross and front sections, and the evidencing of 3D effects on the runup are presented. The extreme runup is estimated for the definition of the design conditions, while the envelopes of the average and minimum runup heights are calculated to assess the normal exercise conditions of existing structures