Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling.

Photovoltaic (PV) power generation is heavily influenced by mismatching conditions, mainly caused by partial or full shading of an array portion. Such a non-uniform irradiation can lead to severe reductions in the power produced; some techniques, such as array reconfiguration or microconverters and microinverters technology are aimed at retrieving this power together with the use of Maximum Power Point (MPP) tracker algorithms, while others tend to mitigate the e ects that power losses have on the PV system, i.e. overheating and aging. Solutions based on the use of bypass diodes and their re-adapted forms belong to this latter case. The complexity of the problem has shown the need of analyzing the role played by each one of the mentioned aspects; the focus of this paper is to give the reader a detailed review of the main solutions to PV arrays shading present in literature.

The power generated by photovoltaic (PV) systems is highly dependent on operating and environmental conditions. In order to increase the efficiency of PV systems, several methods have been developed to track the maximum power point (MPP). This paper proposes an analytical method that introduces a new expression for the extraction of the value of voltage and current at MPP based on the five parameters single-diode model. The method is able to provide the MPP accurately and quickly, thus ensuring that the operating point of the system is always the one that guarantees the maximum efficiency. The accuracy of the proposed method has been evaluated by numerical test, but also verified with the Elgar PV Simulator ET600X. Lastly, the implementation in prototype circuit board EK-TM4C1294XL from Texas Instruments testifies its capabilities for real-time tracking of MPPT.

This work proposes an Artificial Neural Network (ANN) able to provide an accurate forecasting of power produced by photovoltaic (PV) plants. The ANN is customized on the basis of the particular season of the year. An accurate analysis of input variables, i.e., solar irradiance, temperature and air humidity, carried out by means of Pearson Correlation, has allowed to select, day by day, the most suitable set of inputs and ANN architecture also to reduce the necessity of large computational resource. Thus, features are added to the ANN as needed, avoiding waste of computational resources. The method has been validated through data collected from a PV plant installed in ENEA (National agency for new technologies, energy and sustainable economic development) Research Center, located in Casaccia, Rome (Italy). The developed strategy is able to furnish accurate predictions even in the case of strong irregularities of solar irradiance, providing accurate results in rapidly changing scenarios.

A real time simulation of battery conditions is an essential step in the development of energy harvesting devices. Since it is not possible to have a direct measurement, the battery information, such as the remaining charge, need to be estimated by means of model-based estimation algorithms. Most of the existing models describing battery behaviour, are suitable only for a constant discharge current. This paper proposes a study of the dependence of the equivalent circuit model parameters on different discharge conditions. The model presented provides a powerful tool to represent the batteries’ behaviour in energy harvesting systems, involving continuous charge and discharge cycles. The extraction of parameters was performed, starting from a set of reference curves generated in Matlab Simulink environment, referring to Li-ion technology batteries. The parameters were extracted by means of a cascade of global and local search identification algorithms. Finally, the relations describing parameters’ behaviours as functions of the discharge current are presented.

The modeling of photovoltaic cells is an essential step in the analysis of the performances and characterization of PV systems. This paper proposes an experimental study of the dependence of the five parameters of the one-diode model on atmospheric conditions, i.e., irradiance and temperature in the case of thin-film solar cells. The extraction of the five parameters was performed starting from two sets of experimental data obtained from Cu(In,Ga)Se2 solar cells fabricated by the low-temperature pulsed electron deposition technique. A reduced form approach of the one-diode model has been adopted, leading to an accurate identification of the cell. It was possible to elaborate suitable relations describing the behavior of the parameters as functions of the environmental conditions. This allowed accurately predicting the trends of the parameters from a pair of curves, instead of a whole set of measurements. The developed model describing the dependence on irradiance and temperature was validated by means of a large set of experimental measurements on several Cu(In,Ga)Se2 (CIGS) devices built with the same technological process.