With the widespread use of lithium-ion batteries in a wide range of consumer electronics products, the CE industry has undergone a dramatic shift. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved over time to meet the demands of automobiles. International research groups and the performance of production electric vehicles are used to discuss and inform vehicle-driven battery targets. There is still a lot of room for improvement in terms of energy, life expectancy, cost, safety, and fast-charging capabilities for LIBs suited for the automotive sector. In this study, a review of lithium-ion battery applications in electric vehicles is presented.

The primary concerns in the practical photovoltaic (PV) system are the power reduction due to the change in operating conditions, such as the temperature or irradiance, the high computation burden due to the modern maximum power point tracking (MPPT) mechanisms, and to maximize the PV array output during the rapid change in weather conditions. The conventional perturb and observation (P&O) technique is preferred in most of the PV systems. Nevertheless, it undergoes false tracking of maximum power point (MPP) during the rapid change in solar insolation due to the wrong decision in the duty cycle. To avoid the computational burden and drift effect, this article presents a simple and enhanced P&O MPPT technique. The proposed technique is enhanced by including the change in current (dI), in addition to the changes in output voltage and output power of the PV module. The effect of including the dI profile with the traditional method is explained with the fixed and variable step-size methods. The mathematical expression for the drift-free condition is derived. The traditional boost converter is considered for validating the effectiveness of the proposed methods by employing the direct duty cycle technique. The proposed algorithm is simulated using MATLAB/Simulink and validated under various scenarios with the developed laboratory prototype in terms of drift-free characteristics and tracking efficiency. The result proves that the proposed technique can track the MPP accurately under various operating conditions.

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 scarce availability of non-renewable sources and the staggering amount of pollution have inevitably provoked many countries to opt for renewable sources. Thence, invariably, more renewable energy-based applications are hoarded by market stakeholders. Compared to all spheres of renewable energy applications, a considerable part of the energy is pulled into transportation. Wireless power transfer techniques play a significant role in charging infrastructure, considering the current development and advancement in the automotive industry. It will promise to overcome the widely known drawbacks of wired charging in electric vehicles. The effectiveness of wireless charging depends on coil design, compensation techniques, and the airgap between the coils. However, coil misalignment, improper compensation topologies, and magnetic materials reduce the efficacy. We can improve efficacy by overcoming the problems mentioned above and optimizing charging distance, time, and battery size. This paper comprehensively discussed the various electric vehicle charging technologies in conjunction with common charging standards, a list of factors affecting the charging environment, and the significance of misalignment problems. Furthermore, this review paper has explored the suitable coil design structure and different compensation techniques for an efficient wireless charging network.

This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.

The foremost challenge in a microgrid with Distributed Energy Resources (DER) is of managing the intermittent nature of renewable energy sources. Therefore, the extent of integration of the Battery Energy Storage System (BESS) has increased recently in a microgrid due to its versatility, high energy density, and efficiency. Generally, BESS is a grid-tied system and has fast power adjustment capability. Controversially, during the stand-alone mode, it cannot operate in the absence of a local Voltage Source (VS) which acts as a voltage and frequency reference in the network. To ensure the reliable operation of a microgrid during utility grid outage or non-availability of intermittent Renewable Energy Sources (RES), it is significant to operate the BESS with the local VS to dispatch the stored energy. This paper discusses the analytical methodology that can be adopted for identifying the most suitable rating of the VS which can act as a voltage and frequency reference for the BESS using Matlab/ Simulink. Further, a simulation was carried out against various load characteristics and it is observed that an Uninterruptible Power Supply (UPS) with a kVA capacity of 35-45% of that of the BESS with an overload capacity of 150-200% can be chosen as a feasible choice to act as the VS.