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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.

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Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R 2 value is generally obtained which is around 5% greater than the ANN modeling results reported.However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.

Batteries play a vital role in current scenario of energy storage, even though many techniques of energy storage are available, since the time taken to start delivering the stored energy is very less. The battery life time depends upon its charging and discharging characteristics, which are in turn, depend on the internal parameters of battery. These parameters include resistance, capacitance and open circuit voltage. The amount of energy stored in the battery can be calculated by estimating these parameters. In this paper, an optimized model for Lithium ion batteries is presented using evolutionary algorithms to estimate the internal parameters of the battery over different charging and discharging rates. A sample EIG make, 2.5 V, 8 Ahr Lithium ion battery is modeled using two evolutionary algorithms such as genetic algorithm and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for different charging and discharging rates. The results of two algorithms are compared with the catalog values given by the manufacturer in order to identify the appropriate algorithm for battery modeling and validation. This paper concludes that battery characteristics obtained by CMA-ES algorithm match with the measured manufacturer characteristics.

As we all know that lockdown has been announced in the county due to coronavirus which has obstructed students in their studies. In today’s highly competitive world, students face various academic problems including exam stress, disinterest in attending classes, and the inability to understand a subject. Academic stress is becoming increasingly common and widespread among adolescents. This study investigated the academic stress and stress coping strategies on impact of COVID 19 lockdown experienced by the students at University. The sample was drawn through convenient sampling technique and consisted of 250 adolescents studying in the selected colleges at Kattankulathur. Data obtained were analyzed using descriptive statistics, correlation. Results showed that the correlation value -0.167 is highly significant since the p-value is less than 0.01 hence, we can say that there is a highly significant negative correlation between Academic Stress and Coping Strategies”. This study concludes that, if the academic stress score increases then the coping strategy score will be decreasing and vice versa.