SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm

With the rise in an energy crisis, electric vehicles have become a necessity. An integral part of the electric/hybrid vehicle is batteries. Out of many types, Li-ion batteries are providing features like high power as well as energy density. The features make Li-ion is an excellent choice for multiple applications from electronic appliances to electric vehicles. Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential. The monitoring is dependent on actual physical measurements, which are subject to error contributing factors such as measurement noise, errors etc. With the estimation of SOC and State of Health (SoH) of the battery model, the lifetime of the battery will be calculated out, and along these lines sparing significant cost. In this paper, a study on SoH estimation and Li-ion battery SoC is estimated using a Kalman Filter (KF) algorithm estimation and results are presented to validate the Li-ion operating performanc

Cyber-Physical Power System (CPPS): A Review on Modelling, Simulation, and Analysis with Cyber Security Applications

Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.publishedVersio

Evaluation of Mathematical Model to Characterize the Performance of Conventional and Hybrid PV Array Topologies under Static and Dynamic Shading Patterns

The analysis and the assessment of interconnected photovoltaic (PV) modules under different shading conditions and various shading patterns are presented in this paper. The partial shading conditions (PSCs) due to the various factors reduce the power output of PV arrays, and its characteristics have multiple peaks due to the mismatching losses between PV panels. The principal objective of this paper is to model, analyze, simulate and evaluate the performance of PV array topologies such as series-parallel (SP), honey-comb (HC), total-cross-tied (TCT), ladder (LD) and bridge-linked (BL) under different shading patterns to produce the maximum power by reducing the mismatching losses (MLs). Along with the conventional PV array topologies, this paper also discusses the hybrid PV array topologies such as bridge-linked honey-comb (BLHC), bridge-linked total-cross-tied (BLTCT) and series-parallel total-cross-tied (SPTCT). The performance analysis of the traditional PV array topologies along with the hybrid topologies is carried out during static and dynamic shading patterns by comparing the various parameters such as the global peak (GP), local peaks (LPs), corresponding voltage and current at GP and LPs, fill factor (FF) and ML. In addition, the voltage and current equations of the HC configuration under two shading conditions are derived, which represents one of the novelties of this paper. The various parameters of the SPR-200-BLK-U PV module are used for PV modeling and simulation in MATLAB/Simulink software. Thus, the obtained results provide useful information to the researchers for healthy operation and power maximization of PV systems.publishedVersio

Design and Development of Non-Isolated Modified SEPIC DC-DC Converter Topology for High-Step-Up Applications: Investigation and Hardware Implementation

A new non-isolated modified SEPIC front-end dc-dc converter for the low power system is proposed in this paper, and this converter is the next level of the traditional SEPIC converter with additional devices, such as two diodes and splitting of the output capacitor into two equal parts. The circuit topology proposed in this paper is formulated by combining the boost structure with the traditional SEPIC converter. Therefore, the proposed converter has the benefit of the SEPIC converter, such as continuous input current. The proposed circuit structure also improves the features, such as high voltage gain and high conversion efficiency. The converter comprises one MOSFET switch, one coupled inductor, three diodes, and two capacitors, including the output capacitor. The converter effectively recovers the leakage energy of the coupled inductor through the passive clamp circuit. The operation of the proposed converter is explained in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The required voltage gain of the converter can be acquired by adjusting the coupled inductor turn’s ratio along with the additional devices at less duty cycle of the switch. The simulation of the proposed converter under CCM is carried out, and an experimental prototype of 100 W, 25 V/200 V is made, and the experimental outcomes are presented to validate the theoretical discussions of the proposed converter. The operating performance of the proposed converter is compared with the converters discussed in the literature. The proposed converter can be extended by connecting voltage multiplier (VM) cell circuits to get the ultra-high voltage gain

A Comparative Study and Analysis on Conventional Solar PV Based DC-DC Converters and MPPT Techniques

As electricity demand escalated with supply, though there are lot of thermal power station, nuclear energy and other conventional power sources. Yet, there is exhaustion in the above assets and adding dangerous impacts to the atmospheric conditions.  The world searches for sustainable power source that it is normally accessible such as sun and wind. Apart from all the renewable energy resources, solar energy is readily harnessed for domestic application to meet demand. To increase the power conversion efficiency from the solar PV system it is better have a perfect DC to DC converters. The proposed outcome of this paper is to outline the DC to DC converter with MPPT algorithms to concentrate on extreme productivity at roof-top for solar PV application which decreases the cost of energy. In addition to that it also prevents panel miss matching at all environmental conditions for safer DC Voltage with flexible site design especially for domestic applications from the solar photovoltaic module. It is necessary to analyze the converters and MPPT algorithms under closed loop condition for the design and installation of solar PV system to the load or to the grid. This review summarizes few DC to DC converter topologies, maximum power point tracking algorithm and also paid attention on the advantages and disadvantages of these algorithms and topologies

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Abstract The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems

An Improved Quasi-Z-Source Boost DC-DC Converter Using Single-Stage Switched-Inductor Boosting Technique

A modified single-stage Quasi-Z-Source (QZS) boost DC-DC converter using a single MOSFET switch with a single-stage switched-inductor (SI) network is proposed in this paper. The DC-DC converter proposed in this study provides an extra voltage gain compared to the traditional QZS DC-DC converter. An additional capacitor and diode circuit are added to the existing QZS converter to decrease the voltage stress on the MOSFET switch. Therefore, compared to the traditional QZS boost converter, the proposed Modified QZS (MQZS) converter provides large voltage gain under a low duty ratio, less voltage stress, and continuous input current. Moreover, the reliability and the conversion efficiency can be increased. The derivation of the proposed MQZS converter and its operation, selection of parameters, and comparison with another similar converter are discussed in this paper. Lastly, the simulation and experimental results are illustrated to prove the notional deliberations of the proposed MQZS converter

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution