The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.

The performance of the solar photovoltaic system is affected by the unpredictable phenomenon of partial shading. This causes the mismatch losses that suppress the power generation of healthy PV modules in it. The objective of the proposed work in this paper is to bring out the maximum power from each PV module in the PV array by reducing the mismatch losses. A new array configuration method is proposed in this paper, which follows the screw pattern in the row formation. Each PV row is created with distinct PV modules from the rows of the conventional array configuration. This proposed work allows the PV system to operate with minimum mismatch losses by even shade dispersion over the PV array. The proper mathematical expression with all necessary constraints was derived for the array formation of the proposed work. The output analysis is been validated in the simulation of the 9X9 PV array in MATLAB/Simulink®. The mismatch loss generation and output power enhancement are measured and compared with the various conventional array configuration methods under six kinds of partial shading patterns. The proposed array configuration is 40% more efficient than the conventional series-parallel array configuration and also performs better than the total cross-tied and sudoku puzzle pattern methods. The shade dispersion rate of the proposed array configuration has highly reduced the mismatch losses in the PV system and hence, it improves the power output.

This article presents simulation and experimental results for add-on transmission attachments that have been developed for the scanning electron microscope(SEM). A primary beam energy of around 30 keV is used to irradiate thin test specimens (<100 nm). A transmission lens attachment enables a conventional field emissionSEM to improve its image resolution by an order of magnitude, providing an image magnification of several million and a spatial resolution down to 0.3 nm. In addition, a compact energy electron loss spectrometer attachment has been developed that uses the SEM’s built-in secondary electrondetector.

This paper presents a boost DC-DC converter topology with non - isolated high gain and output midpoint, to boost the voltage obtained from solar photovoltaic panels. The three-level boost converter is coupled to the output port of the single-switch quadratic boost converter to derive the proposed converter topology. The voltage gain of the proposed converter is greater than that of the classical boost converter. The voltage stress on the switches of the proposed converter is equal to half of the converter output voltage. Static analysis, operating modes, experimental waveforms in continuous current conduction and discontinuous current conduction modes are shown. A 520 W prototype converter was implemented in the laboratory and its results are presented.

An enhanced two diode photovoltaic (PV) model is proposed to estimate accurately the current-voltage (I-V) characteristic curves of the PV modules. In the proposed method, only four parameters of the two diode PV model are needed to calculate from seven parameters with reasonable computational effort. In this paper, a new expression is developed to find the value of reverse saturation current (I_sat) using two diode PV model's I-V characteristic curve equation considering the effects of temperature and irradiation on the value of shunt and series resistances. A Numerical technique is used to determine the value of output current, series and shunt resistance of the proposed PV model. For validating the proposed PV model, five different PV modules are tested at various weather conditions. The proposed PV model is simple and accurate and it is useful to design the suitable MPPT converter.