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.

Recent studies proved that Ferrite Core (FC) power inductors working in sustainable saturation conditions can enable the achievement of switch-mode power supplies with high power density levels. Since the saturation characteristic of these magnetic components is strongly non-linear, mathematical models capable of representing FC inductors non-linear behavior are extremely valuable. This modelling problem can be of considerable complexity, especially in case of sharp saturation profiles. Neural networks are structures of extreme topological flexibility, intrinsically non-linear and able to operate on multi-dimensional data both in input and in output. Their ability to be universal approximators has also been proved, since a neural structure of adequate topological characteristics has been shown to have the potential to well approximate any multi-dimensional non-linear function. In this paper, we propose the use of a feedforward neural model to represent the behavior of FC power inductors up to deep saturation current levels. The technique has been verified on commercial FC power inductors via numerical simulations, thus allowing the validation of the proposed neural model. The results obtained open up the possibility of including the problem of modelling the non-linear characteristic of inductors in the great field of deep learning research.

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.

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.

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 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 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.