Harmonic elimination at the fundamental frequency is very much appropriate for high and medium range of power generation and applications. This paper considers a new technique for selective harmonic elimination (SHE), in which the total harmonic distortion (THD) is minimized when compared with that of the conventional one. With this technique, the harmonics at lower order are eliminated, which are more predominant than the higher ones.Cascaded H-Bridge inverter fed by a single DC is considered which is simulated with the switching angles generated by both the conventional method of SHE and the new method of SHE. The simulated results of the load voltage and the waveforms of the harmonic analysis are shown. The THD values are compared for the two techniques.  The experimental results are also shown for the new technique. The switching angles are generated with the help of field programmable gated array (FPGA) in the hardware. The value of experimental THD of voltage is compared with that of simulated THD and the comparison prove that the results are satisfactory.

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Summary Balancing the lithium‐ion battery pack is essential to enhance the energy usage and life cycle of the battery. This paper analyses passive cell balancing method of Li‐ion battery for e‐mobility application based on the energy loss and cost estimation through simulation and hardware implementation. As a part of the simulation, an electrical equivalent circuit battery model is developed and model parameters are obtained using electrochemical impedance spectroscopy test. The experimented passive balancing topologies include a switched shunting resistor circuit with appropriate control logic. Final voltage‐based balancing algorithm is implemented to balance the cells at high state of charge. Experimental results are compared against the theoretical investigation. Based on the outcome of this experiment, the most significant characteristics of the passive balancing system can be developed by considering the impact on the battery performance, energy loss, and cost.

Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

The shrink in accessibility of petroleum products and increment in asset request are eventual outcomes for Electrical Vehicles (EVs). The battery has an impact on the performance of electrical vehicles, the driving range. Lithium ion (Li-ion) chemistry is extremely sensitive to overcharge and deep discharge, which can harm the battery, shortening its period of time, and even inflicting risky things. The Battery Management System (BMS) comprises of the consequent parts: management, equalization and protection. Of the three components, equalization is that the most crucial with respect to the durability of the battery framework. The ability of the full pack diminishes rapidly amid the procedure which leads to degradation of the full battery framework. This condition is extreme once the battery incorporates a more number of cells in series and frequent charging is conveyed through the battery string. The cell imbalance during charging, discharging is a major issue in battery systems used in EVs. To circumvent the cell imbalance, cell balancing is used. Cell balancing enhances battery safety and extends battery life. This paper discusses about different active balancing method to increase the life span of the battery module. Based on the comparison, the inductor based balancing method for 60V battery system is implemented in the MATLAB/Simscape environment and the results are discussed.