Study and Implementation of Compact Modeling Techniques for the Energy Analysis and Optimization of Complex Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

A method to empirically set the coefficients of an analytical model of battery aging

The increase of battery-powered devices and systems, from consumer electronics to electric vehicles, means that the reliability analysis of such systems concerns both energy cells and battery packs, as well as circuitry. For this reason, battery modeling has been one of most developed research areas during the last two decades. In this context, the Millner model, which is based on crack propagation theory, provides an accurate mathematical model for the analysis of the degradation (i.e., aging or capacity fading) of lithium-ion cells. In order to extend the application of the Millner model in a practical and simplified way, this work describes a method to empirically set the coefficients of this model by extracting these values from experimental data only published by the manufacturer and some researchers. When simulating an AMP20m1HD-A prismatic pouch cell at different working and operating conditions, the results show an accurate analysis of the capacity fading, with an error of generally less than 3%

A cost of ownership analysis of batteries in all-electric and plug-in hybrid vehicles

Exploring legal documents such as laws, judgments, and contracts is known to be a time-consuming task. To support domain experts in efficiently browsing their contents, legal documents in electronic form are commonly enriched with semantic annotations. They consist of a list of headwords indicating the main topics. Annotations are commonly organized in taxonomies, which comprise both a set of is-a hierarchies, expressing parent/child-sibling relationships, and more arbitrary related-to semantic links. This paper addresses the use of Deep Learning-based Natural Language Processing techniques to automatically extract unknown taxonomy relationships between pairs of legal documents. Exploring the document content is particularly useful for automatically classifying legal document pairs when topic-level relationships are partly out-of-date or missing, which is quite common for related-to links. The experimental results, collected on a real heterogeneous collection of Italian legal documents, show that word-level vector representations of text are particularly effective in leveraging the presence of domain-specific terms for classification and overcome the limitations of contextualized embeddings when there is a lack of annotated data

Forecasting the grid power demand of charging stations from EV drivers’ attitude

In recent years there has been a significant increase in the production of electric vehicles (EVs), in the global strive to reduce polluting gases produced by conventional fossil-fuel driven vehicles. Therefore, many optimization algorithms have been proposed for EV mobility and the charging of battery packs in the stations connected to power grids. However, there are situations in which experimental results are not sufficient, and simulations are needed. In this work, we address the effects of the charge demands of an EV fleet on the grid by considering the attitude of EV drivers, and especially their range anxiety. This influences their decision of when to recharge the battery pack. To this end, an agent-based model has been developed for the simulation of a power grid considering different scenarios based mainly on the state of charge (SOC) of battery packs at the time of the charging requests of EVs at service stations. The results indicate that in general a high battery SOC at the beginning of charging increases the probability of reaching higher power peaks on the grid

Solar energy potential assessment: An overview and a fast modeling approach with application to Italy

Exponential growth of photovoltaic installations in several countries represents a strong motivation for investments in renewable energies. This paper provides an overview on current methodologies for assessing the photovoltaic potential, with the aim of supporting the selection of optimal sites in a given region of interest. With a special focus on the Italian context, an additional goal of this work is to show that, fast and accurate estimates of the power of new photovoltaic installs can be obtained by detecting available surface areas (e.g. by cadastral maps or image analysis). Basic average solar radiation and temperature for some specific areas can be indeed obtained from the available solar maps reported in the geo‐databases of the Joint Research Centre of the European Commission (JRC). On the basis of such data, as an alternative to a query in the on‐line Photovoltaic Geographical Information System ‐ PVGIS ‐ (the web‐based reference tool for the performance assessment of photovoltaic plants in Europe and Africa), the use of simple and appropriate polynomials is proven to be suitable for a quick analysis of solar energy potential applications

A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

A case for a battery-aware model of drone energy consumption

The market of small drones has been recently increasing due to their use in many fields of application. The most popular drones are multirotors, in particular quadcopters. They are usually supplied with batteries of limited capacity, and for this reason their total flight time is also limited.As a consequence of the non linear characteristics of batteries, estimation of the real flight time may become an issue, since most battery models do not include all the non idealities. Consequently, applications such as delivery service, search and rescue, surveillance might not be accomplished correctly because of inaccurate energy estimations.This paper describes a battery-aware model for an accurate analysis of the drone energy consumption; this model is then applied to a scenario of drone delivery. Results show an accuracy greater of about 16% with respect to the traditional estimation model