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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.

Flexible Alternating Current Transmission System devices have numerous applications in electrical transmission lines like improvement of voltage stability, reactive power compensation, congestion management, Available Transfer Capacity enhancement, real power loss reduction, voltage profile improvement and much more. The effectiveness of these FACTS devices is enhanced by the placement of these devices in the transmission lines. The placement is based on transmission line sensitivity factors such as Bus voltage stability index and line voltage stability index. This research article focuses on optimizing the location, number and ratings of FACTS devices using Evolutionary Algorithms like Bacterial Foraging Algorithm and Gravitational search algorithm. FACTS devices such as Static Var Compensator, Thyristor Controlled Series Capacitor and Unified Power Flow Controller are placed on IEEE 14 bus and IEEE 30 bus systems for reducing the real power loss in the transmission system. The results show that the performance of the transmission lines is e nhanced more using Bacterial Foraging Algorithm than Gravitational Search Algorithm.

Power conversion efficiency is the most important factor to be considered in PV systems because it is affected by various environmental conditions. The effect of partial shading is the most influenced factor in the reduction of power output. Various research schemes like Maximum Power Point Tracking (MPPT), array configuration scheme, reconfiguration, etc., work on the PV system to reduce the impact of partial shading. This paper presents a new kind of array configuration scheme that forms the PV array based on the moves of the Knight coin in the chess game. This arrangement creates the squared PV array of rows with distinct PV modules which is capable of evenly dispersing the shading in the partially shaded PV array. Also, this scheme is applicable for the non-squared PV arrays to create PV rows with the PV modules from a distinct location or from the same row with optimized distance to disperse the maximum level of shading. The proposed method has been discussed with the proper mathematical formulation with all necessary constraints and also it been validated with the hardware arrangements and MATLAB/Simulink ® model.

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The optimization problem with a single objective can obtain a single solution, called an optimal solution. It maximizes or minimizes the performance of a particular objective function to a given constraint. But, in the case of the multi-objective optimization, different objectives can be simultaneously optimized. Thus, this paper recommends a multi-objective optimization methodology for simultaneously perform the two objective functions such as resizing and optimal placement of Distributed Static Compensator (DSTATCOM) for reducing the power loss, total cost and enhancing the voltage profile. For these purposes, an integrated approach of two optimization algorithm called Multi-objective Ant Colony Optimization (MACO) and Bacterial Foraging Optimization Algorithm (BFOA) are used. The prime intention of this work is to bring down the power loss, total cost and enhance the voltage profile by placing the DSTATCOM device in an optimal location. Here, IEEE-30 and IEEE-69 bus systems are considered to appraise the recital of the recommended approach. Moreover, the effectiveness of the MACO-BFOA approach is evaluated and compared with other multi-objective algorithms. From this analysis, it is observed that when compared to these techniques, the proposed system provides the minimized power loss and total cost.

Summary In this article, a novel hybrid method is proposed to optimally manage the energy for a hybrid electric vehicle system. The proposed technique is the joint execution of both the Kernel Wingsuit Flying Search Algorithm and Sea Lion Optimization Algorithm, hence it is called WF2SLOA. The main objective of the WF2SLOA method is integrated in the energy management system to split the torque between the engine and electric machine. During the WF2SLOA‐based energy management development, this article performs a parametric investigation on numerous main factors, such as state types and number of states, states and action discretization, exploration and exploitation, and learning experience selection. The proposed method is implemented in MATLAB/Simulink, and the performance is assessed with the existing methods. Consequently, the outcomes illustrate that the selection of the learning experience can diminish the fuel consumption of the vehicle. Furthermore, the states and action discretization study indicates the fuel consume of the vehicle diminishes as action discretization enhances while raising the states discretization is harmful to the fuel consume. The maximizing count of states also raises the economy of fuel. Thus, the simulation outcomes show that the performance of the proposed method is more efficient than the existing methods. The mean, median, and SD of the WF2SLOA method attains 1.5420, 1.5043, and 0.0509.

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