Nowadays, energy estimation in various application areas is a major research topic. Additionally, various machine learning techniques, especially regression methods and artificial neural networks, have been developed in recent decades to improve the accuracy of such estimates. This article presents a nonlinear compact regression model for estimating the yearly solar irradiation in Africa and Europe by considering only the latitude and mean temperature of the locations as input parameters. The definition of the values of the coefficients is based on the least-square method constrained by the maximum absolute error. The results of 16 conventional regression models, using the same number of predictors, were compared with the result of the model proposed. Our model minimizes the root mean square error by at least 15%.

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.

The modeling of electrical energy storage systems has received a lot of attention during the last two decades, as a consequence of the remarkable increase of battery-powered devices. However, automated extraction of a cell/pack characteristics from available product data, has been proposed only more recently. Although the automated modeling of a battery discharge performance is already well reported in the literature, extraction of the basic features of the charging phase is less common since most commercial chargers work at the standard constant current-constant voltage (CC-CV) protocol at fixed working conditions. Nevertheless, many rechargeable batteries allow different charging current rates. Therefore, for a full modeling and analysis of the battery behavior during the charging phase, the basic features, like the internal resistance and the open circuit voltage (Rch and VOCch), should be modeled. Unfortunately, only a few data regarding the charging phase are available from time-based plots in datasheets. This work presents a method for modeling the fundamental characteristics of the charging phase of a battery, starting from typical multi-plot time-based charts.

Drones are becoming increasingly popular in the commercial market for various package delivery services. In this scenario, the mostly adopted drones are quad-rotors (i.e., quadcopters). The energy consumed by a drone may become an issue, since it may affect (i) the delivery deadline (quality of service), (ii) the number of packages that can be delivered (throughput) and (iii) the battery lifetime (number of recharging cycles). It is thus fundamental try to find the proper compromise between the energy used to complete the delivery and the speed at which the quadcopter flies to reach the destination. In order to achieve this, we have to consider that the energy required by the drone for completing a given delivery task does not exactly correspond to the energy requested to the battery, since the latter is a non-ideal power supply that is able to deliver power with different efficiencies depending on its state of charge. In this paper, we demonstrate that the proposed battery-aware delivery scheduling algorithm carries more packages than the traditional delivery model with the same battery capacity. Moreover, the battery-aware delivery model is 17% more accurate than the traditional delivery model for the same delivery scheme, which prevents the unexpected drone landing.

In this work, the implementation of a compact electro-thermal model of a trench gate field-stop IGBT device is presented. The primary target is to address system-level mixed signal multi-domain simulation where the simulation time is a key factor for the usefulness of results. The methodology adopted provides a model suitable for use on both SPICE-only simulators, where thermal behavior is mapped to electrical quantities, and on a multi-domain environment, where heterogeneous physical domains are managed by a simulation engine. A comparison with real device characteristics is presented to show model accuracy and finally a simulation of an inverter circuit is investigated to highlight achievable performances.

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.

The aging of a rechargeable battery is mainly due to stress during charge-discharge cycles. Although the discharge phase is difficult to control, the charging phase can be performed in a specific way in order to mitigate the aging of the battery during its usage. It therefore becomes important to select the correct charging algorithm. In the case of mobile systems, equipped mainly with lithiumion batteries, the standard widely adopted for charging a battery is the typical constant current/constant voltage (CC-CV) protocol usually based on a linearly regular charge process. In this work, we propose a charging protocol based on the standard CC-CV method in which the charge start time and the value of the charging current can be programmed in such a way that the aging of the battery is mitigated. To validate this charging scheme we use an aging model that includes the charge/discharge current among the major parameters, and an analytical macro-model for the CC-CV charge time analysis.

The evaluation of the cycle life of batteries is an essential task in the assessment of the reliability and cost of battery-operated devices. Several compact cycle life models have been proposed in the literature, that exhibit a general trade-off between generality and accuracy. Some models are based on a compact equation derived from experimental data and try to extract a general relationship between cycle life and the relevant parameters (mostly the depth of discharge), but suffer from poor accuracy. At the other extreme, more accurate models, based on incorporating the aging effect into an equivalent circuit, tend to be focused on a specific device and are seldom applicable to another battery. In this work we propose an equation-based model that tries to overcome the accuracy limits of previous similar models. The model parameters are obtained by fitting the curve based on information reported in datasheets, and can be adapted (with different accuracy levels) to the amount of available information. We applied the model to various commercial batteries for which full information on their cycle life is available. Results show an average estimation error, in terms of the number of cycles, generally smaller than 10%, which is consistent with the typical tolerance provided in the datasheets, and much lower than previous equation-based models.

The market of portable devices, wireless sensors, electric vehicles and storage systems has grown enormously in recent years. As a consequence, batteries and related technologies have become one of the major topics for researchers. Due to the large variety of applications in which batteries are involved, battery modeling is becoming an extremely important research topic. This relevance is witnessed by the number of papers addressing battery modeling.

The de-facto standard approach in battery modeling consists of the definition of a generic model template in terms of an equivalent electric circuit, which is then populated either using data obtained from direct measurements on actual devices or by some extrapolation of battery characteristics available from datasheets. These models typically describe only intra-cycle effects, that is, those manifesting within a single charge/discharge cycle of a battery. However, basic battery dynamics, during a single discharge, cannot provide a true estimate of the actual lifetime of the battery, e.g., how its usability decreases due to long-term and irreversible effects, such as the fading of capacity due to aging or to repeated cycling. While some solutions in the literature provide answers to this problem by proposing suitable models for these effects, they do not provide solutions for how to incorporate them into a generic model template. In this work we propose a method to include inter-cycle battery effects into a reference model template in an automated way, and using solely data reported by battery manufacturers. Flexibility and accuracy of the proposed strategy are demonstrated by modeling a commercial lithium iron phosphate battery, whose datasheet provides long-term capacity fading information.

Availability of a simulation model of a battery is one of the most important requisites in the system-level design of battery-powered systems. The vast majority of the models describe the discharge behavior of the battery; so far, the estimation of charging time has been in fact only marginally studied because the charging phase is regarded as a relatively controlled process compared to discharge. In this paper, we present a compact macro-model for the estimation of charging time under the most widely used charge protocol, i.e., Constant Current-Constant Voltage (CC-CV). This model is derived under the consideration of the context of the existing models including the well-known Peukert's law and equivalent electric circuits. The estimation result with the proposed model based on the manufacturer's data of commercial Li-ion batteries shows fair accuracy, especially when compared to estimates on parameters extracted from discharge characteristics.

Models based on an electrical circuit equivalent have become the most popular choice for modeling the behavior of batteries, thanks to their ease of co-simulation with other parts of a digital system. Such circuit models are actually model templates: the specific values of their electrical elements must be derived by the analysis of the specific battery devices to be modeled. This process requires either to measure the battery characteristics or to derive them from the datasheet. In the latter case, however, very often not all information are available and the model fitting becomes then unfeasible. In this paper we present a methodology for deriving, in a semi-automatic way, circuit equivalent battery models solely from data available in a battery datasheet. In order to account for the different amount of information available, we introduce the concept of “level” of a model, so that models with different accuracy can be derived depending on the available data. The methodology requires only minimal intervention by the designer and it automatically generates MATLAB models once the required data for the corresponding model level are transcribed from the datasheet. Simulation results show that our methodology allows to accurately reconstruct the information reported in the datasheet as well as to derive missing ones.

Green transport for improving air quality is essential and urgent goal for reaching a healthy environment. In towns with large fleets of public vehicles, technology transfer from standard into new and better solutions requires, in general, time and great investments. This paper presents a quick retrofit for conventional buses in urban transport in order to reduce fuel consumption by using photovoltaic (PV) panels that only recharge the original bus batteries. Experimental tests show that this solution is really effective. Indeed, it could save, per year, several hundred liters of diesel fuel for each bus after considering a solar energy production of about 1.4 MWh.

The increasing demand of "safe" vehicles requires continuous design of innovative devices and sensors. This paper presents a methodology for an efficient energy analysis of a self-powered sensor in an ultra-low power automotive application. In order to achieve this goal, new tools have been developed for storing and elaborating data (e.g., power consumption values, operating conditions, etc.) and even for reporting the energy balance, after considering the source (i.e. a scavenger device) that supplies the sensor.

Serial data streaming, one of the most important functions in modern communication systems, is becoming more and more power consuming as bit-rate is increasing without standstill. In this work, we propose a novel technique for partitioning conventional N-bit registers in standard data converters, in order to reduce their switching activity, and therefore power consumption. The architecture here presented have a very low area overhead with respect to the standard ones for serializers and, furthermore, it allows different (i.e., custom) configurations for the partitioning. The proposed method even allows to extract idleness conditions of register banks in order to apply the well-known clock-gating technique to the circuit and thus furtherly reducing the total power consumption. This method has been applied to different data converters (i.e., serializers) in a base-band radio within an ultra low-power industrial design and the results highlight the effectiveness of the proposed technique.

This paper presents a low-power bus encoding technique suitable for the digital interface to a Liquid Crystal Display (LCD). In particular, we focus on interfaces that are compliant to the Digital Visual Interface (DVI) standard, in which the three color channels are serially transmitted to achieve high bandwidth.The proposed technique exploits the well-know inter-pixel correlation that exists in typical images by serially transmitting an encoded representation of the difference between adjacent pixels. The encoding is based on the principle of clustering the 1's in the code towards either ends of the pixel data, in such a way that serial transmission of a code yields at most 1 transition per pixel.The application of the encoding to a series of standard images resulted in energy savings of around 60% on average, with respect to a plain transmission of 8-bit pixel data.

We propose a class of low-power codes, called Limited Intra-Word Transition (LIWT) codes, suitable for the digital interface to Liquid Crystal Displays (LCD).The proposed technique exploits the existing inter-pixel correlation of typical images, by transmitting an encoded representation of the difference between adjacent pixels. Since all standard LCD transmission protocols are serial, the LIWT specifically targets the minimization of intra-word transitions.The application of the encoding to a series of standard images resulted in transitions savings over 60% on average with respect to two standard TMDS and LVDS protocols.