This paper investigates and compares the performance of various behavioral modeling approaches, both analytical and machine learning-based, for Lithium-ion batteries. The analytical models rely exclusively on the genetic programming algorithm, while the machine learning-based models employ several well-known regression techniques, including multi-layer perceptron, support vector machine, and gradient boosting. These data-driven models are used to relate the battery's terminal voltage to its state of charge, charge/discharge rate, and temperature, using a consistent dataset for the case study. The study focuses on the transient discharge phase of a Lithium Iron Phosphate battery under realistic operating conditions, with a state of charge between 20% and 80%, discharge rates ranging from 0.25C to 1C, and temperatures between 5°C and 35°C.

This paper proposes a novel approach to derive analytical behavioral models of Lithium batteries, based on a Genetic Programming Algorithm (GPA). This approach is used to analytically relate the battery voltage to its State-of-Charge (SoC) and Charge/discharge rate (C-rate), during a battery discharge phase. The GPA generates optimal candidate analytical models, where the preferred one is selected by evaluating suitable metrics and imposing a sound trade-off between simplicity and accuracy. The GPA proposed model can be seen as a generalization of the equivalent circuit models currently used for batteries, with the possible advantage to overcome some inherent limits, like the extensive laboratory characterization for model parameters evaluation. The presented case-study refers to a Lithium Titanate Oxide battery, with SoC values going from 5 to 95%, at C-rate values between 0.25C and 4.0C.

This paper discusses a design procedure of damped differential-mode input filters for DC-DC switching regulators, allowing to minimize the size of the filter and damping inductors and of the filter capacitors. The proposed approach simplifies the identification of the smallest commercial components complying with noise attenuation, stability and efficiency requirements, while ensuring optimal damping of the filter. The experimental results presented in the paper validate the proposed design method.

In this paper, an analytical behavioral model is proposed to describe the dependence of the mutual inductance on the real trajectory of an electrical vehicle, recharged by a dynamic Wireless Power Transfer (WPT) system. The model is derived by means of a multi-objective genetic programming algorithm, which uses a training data set provided by a 3D magneto quasi-static numerical solver. The model is herein applied to a real WPT system working at 85 kHz and successfully validated.

This paper discusses the optimal matching of the power inductor for the buck-boost converter. The relationship between the amplitude of the inductor peak-peak ripple current and the input/output operating conditions of the buck-boost converter facilitates the use of smaller ferrite core power inductors. The analysis and the results of experimental tests presented in this paper prove the possibility of reducing the size of ferrite core power inductors in the buck-boost converter by exploiting partial saturation.

In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (IC) application is proposed. The inherent lack of precise physical IGBT loss model and the uncertainty of load in IC application has stimulated the idea to identify system-level behavioral power loss models that allow to cover a variety of devices and load conditions. For this goal, a Genetic Programming approach has been adopted, that starts from measured electrical quantities and returns a set of models, each one with the same structure but with different parameters relevant to the device under test. The models generated by the proposed method based on a training set of case studies have been merged into a generalized model and verified through a validation set.

This paper discusses the identification of power loss models of ferrite core power inductors for high-power-density Switch Mode Power Supplies. A novel method, based on Genetic Programming (GP) approach, is herein proposed. It is aimed at discovering new loss models, starting from experimental measurements and taking into account all the operating conditions, such as switching frequency, inductor current ripple and volt-microsecond product, average and rms inductor current values, even for possible inductor operation in partial saturation. The behavioral models obtained by means of the GP approach are in good agreement with experimental measurements.

This paper discusses a Feedback Injection Technique (FIT) to simplify the line/load transient and AC response testing of dc-dc power supplies. The FIT exploits the power summing amplifier feature of op-amps embedded in the feedback control loop of dc-dc regulators, thus realizing a dynamic source and a dynamic load to observe the transient and AC response of another power supply connected to its input or output by means of an oscilloscope. Examples of line transient and load transient tests and PSRR measurements are presented in the paper, realized with the Texas Instruments PMLK Series BUCK and LDO boards, allowing an easy implementation of dynamic testing of power supplies based on the FIT concept.

Identification of saturation curves of Ferrite Core Inductors (FCIs) based on Differential Evolution Algorithms Processing of Experimental Measurements (DE-EMP) is discussed in this paper. It is shown that the DE-EMP approach provides more realistic data for the prediction of the peak-to-peak ripple current of FCIs compared with the information provided by manufacturers. An experimental validation of the proposed method is presented in the paper, relevant to a step-down dc-dc power converter application.

This paper discusses models and methods for the analysis of PV systems with Distributed Maximum Power Point Tracking (DMPPT). An Energy Productivity Analysis Algorithm (EPAA) is discussed, integrating models and algorithms from component level to system level, allowing the analysis of PV systems operating in partial shading and electrical mismatched conditions. The EPAA provides realistic assessment of the energy productivity including the effects of PV panels characteristics, of shadows and of the MPPT converters. A comparative evaluation of boost-based vs buck-boost-based DMPPT solutions is presented in the paper, highlighting how the energy productivity of DMPPT PV systems is influenced by the parameters of real components.

This paper discusses the effect of Constant Power Loads (CPLs) on the stability of a Smart Transformer (ST). The main characteristic of a CPL is that its current decreases when its voltage increases and vice versa. Therefore, in small signal analysis a CPL behaves as a negative impedance, and as a consequence it can impact the system stability. The aim of this work is to establish how it is possible to stabilize the system by acting on the filter parameters and the control strategy. A distributed power generating systems supplied by a ST has been considered as case study. The analysis and the simulation results show that it is not possible to stabilize the system by using standard controllers without an appropriate selection of the output filter components.

This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method for parameters identification is proposed, based on Evolutionary Algorithms (EAs) and on the analysis of inductors non-linear behavior. Two EAs, the Genetic Algorithm and the Differential Evolution, are investigated and compared. The results of the proposed method are experimentally validated by means of a buck converter evaluation board.

The hysteresis in the state-of-charge (SoC) vs. open-circuit voltage characteristic of a lithium-iron-phosphate (LiFePO4, LFP) battery is modelled with two approaches. The first one is based on a first-order charge relaxation equation, the second one is the Preisach model implemented with the Everett function. The advantages and drawbacks of the methods are discussed. Simulation results are compared for a 20 A h LFP cell, stimulated with various SoC evolutions, allowing us to draw minor loops in the SoC-OCV plane. The results are also compared to experimental data.

This paper aims at investigating and modelling the hysteresis in the relationship between state-of-charge and open-circuit voltage of lithium-iron-phosphate batteries. A first-order charge relaxation equation was used to describe the hysteresis dynamics. This equation was translated into a voltage-controlled voltage source and included within an equivalent electric circuit of the battery used in online state-of-charge estimators. The effectiveness of the obtained battery model was verified comparing simulated and experimental data.

The goal of this paper is to derive the analytical stability boundaries for Ćuk converter and to highlight the joint impact of power stage passive components and current controller characteristics on the converter stability. Stability conditions are determined by means of a novel dynamic model of the Peak Current Controlled (PCC) Ćuk converter, taking into account the difference between the small signal ac components of the voltage across coupling capacitor and of the sum of input and output voltage. A reduced-order small model of PCC Ćuk converter is proposed.

The European policies are encouraging self-consumption and energetic autarchy of the distributed generation system. This is calling for the development of a next-generation of photovoltaic (PV) inverters for residential applications, equipped with battery storage unit and load management embedded tools. This paper presents the results of a system-level analysis realized by means of a model and a dedicated algorithm, aimed at investigating the efficacy of high-level battery energetic management rules and load allocation strategies. In particular, the behavior of a residential PV inverter equipped with a battery system and load manager is analyzed in the framework of incentive policies peculiar of European countries. The effects of constraints on the load management and of battery sizing are also highlighted.

This paper deals with the design of a PhotoVoltaic (PV)-fed Light Emitting Diode (LED) lighting system including an energy storage unit. The system is aimed at exploiting the generated energy in the absence of sunshine, provided that the power absorbed by the LED load can be varied within a power range corresponding to a sufficient lighting brightness. The main focus of this work is to discuss issues related to the sizing of the main components of the lighting system, i.e., the PV panel and the battery, for a dimmable LED load. The paper proposes a battery State Of Charge (SOC)-based LED dimming allowing to minimize the components' size. The discussion is based on the minimization of the following two quantities: 1) the excess of energy produced by the PV source, which the battery cannot store, and 2) the missing energy that the battery cannot deliver to the load. The application of the proposed methodology to a PV-fed LED-based street lighting system with dimmable LED load are presented and discussed.

This work presents a single-stage photovoltaic inverter which is based on one-cycle control. Such an inverter is characterized by two original features. The first one lies in a new formulation of the set of equations for the design of the one-cycle control circuitry of the inverter, such constraints are explained in L. Egiziano et al. (2006) and are here used to select the optimal solution. The second original characteristic of the proposed inverter lies in the development of a Perturb&Observe maximum power point tracking technique customized for a one cycle controlled single-stage inverter (patent pending) based on a sub set of the constraints reported in L. Egiziano et al. (2006). The results obtained show that the performances of the proposed system are top level and that the proposed solution represents the first true single stage photovoltaic inverter

In this paper it is shown, by means of suitable numerical simulations and laboratory experiments, that one-cycle control of switching converters operating in deep DCM does not work properly. A simple solution is proposed in order to allow the right working of the system even under almost no-load conditions

In this paper a new method for the analysis of soft synchronous commutations in switching converters is presented. The method is based on the application of the compensation principle, and allows one to perform jointly the analysis of both hard and soft synchronous commutations. In the paper it is also shown that the proposed method enables one to highlight and explain some mechanisms, underlying soft synchronous commutations, that have not yet been evidenced in literature. In particular, it is shown that a soft commutation of a switching device can trigger the synchronous commutation of other switching devices, provided that proper conditions are met. For the analysis of such situations the derivatives of the currents and of the voltages of switching devices need to be watched. An example concerning the analysis of a ZCT dc-dc switching converter is discussed.

Synchronous commutations (SC) occur in switched LCR circuits whenever two or more switching devices change state simultaneously. SCs are the basic feature of switched converters and their analysis is a key point in circuit simulation. The method presented in this paper is aimed at providing a simple and general tool useful both for theoretical inspection and numerical analysis of commutations in switching converters, even in presence of controlled sources. The method is based on the compensation principle and on a reduced hybrid resistive model of the circuit valid across the commutations. An example of hard commutations analysis in a voltage regulated dc-dc converter is presented.