The automobile sector is a promising avenue for enhancing energy security, economic opportunity, and air quality in India. Before penetrating a large number of electric vehicles (EV) into the power grid, a thorough investigation and assessment of significant parameters are required, as additional nonlinear and EV loads are linked to the decentralized market. Many automobile companies have already invested in electric vehicle research; hence, a detailed analysis on range anxiety and grid connectivity concerns are the important factors affecting the future of the electric vehicle industry. In this paper, the initial review is about the decentralized market in India and sustainable aspects of electric mobility based on the Indian context, as it is a developing nation with an enormous resource and scope for EV markets. With recent literature from the last three years, the substantial constraints observed in benefits and challenges are reviewed. The financial stability aspects and the incentives to overcome the barriers to EV adoption are briefly discussed. From the review, it has come to the limelight that infrastructure availability, technology, load demand, and consumer behaviour are all major obstacles in the electric vehicle ecosystem. For the overall design and study of the vehicle to grid (V2G) infrastructure, this paper also provides insight into the representation of electric vehicles in different energy-efficient models and their categorization while connecting to the grid. The methodology adopted for energy-efficient models includes lifecycle emissions, economy, smart charging, real-time optimization, aggregated EV resource modelling, and a support vector machine (SVM)-based method. This paper gives a positive impact on EV fleet integration and electric mobility in general, as it critically reviews the influential parameters and challenges. This classification depends on crucial parameters that are at the frontline of EV grid integration research. This review is a solution to enhance grid stability in regard to new EV models. With the advanced electric motors development and renewed battery technology models, longer-distance automobiles are now available on the market. This paper investigates the constraints of EV grid integration and analyzes different EV models to ease the grid stability for a decentralized market.

The large-scale integration of microphasor measurement units (μPMUs) is inevitable in smart grids due to the enhanced demand response strategies of active distribution networks and renewable energy sources. As synchrophasor data is crucial, with higher data volume and complexity, a comprehensive method is essential to decode real-time spatiotemporal anomalies and grid events precisely. This paper presents a comprehensive methodology for real-time synchro phasor forensic analysis by leveraging historical frequency and spectrum data. The proposed multi-stage framework investigates the integration of statistical time-frequency analysis with Autoregressive Integrated Moving Average (ARIMA) and machine learning (ML) to address the challenges in grid management. The first stage focuses on preprocessing raw data from New England ISO and the European continental dataset, as it contains multiple events. After denoising and feature extraction by multiresolution analysis, the ARIMA model measures the rolling standard deviation (RSD) for frequency components within different sliding window frames. With a proposed event detection index, the synchrophasor event detection algorithm (SPEDA) detects frequency excursions, oscillatory events, and other event categories using statistical thresholds. During the second stage, the retrieved features and intensity of the detected events are cross-validated using ML models, enhancing the overall effectiveness of the study. A meticulous scrutiny of ML classifiers and their evaluation metrics is performed, and it is found that the Extreme Gradient Boosting (XGB) performs at the overall best. The proposed method has been investigated and proven effective in this integrated approach with advanced computational facilities to expedite complex computations and reduce the training time.

Energy-efficient storage systems are crucial in the rapidly evolving electric vehicle (EV) technology landscape. This study presents an innovative strategy for real-time charging of deep-cycle batteries by integrating solar photovoltaic (PV) panels with power from the grid. The model relies on the effects of the Urban Dynamometer Driving Schedule (UDDS) and aims to determine the optimal deep-cycle battery capacity sizing. The initial phase involves designing the dimensions of the valence batteries, each including four parallel cells with a capacity of 59Ah. The adequate capacity of the battery pack is determined by evaluating the energy demands, considering the average duration of daylight, existing weather conditions (derived from Bahia Blanca, Argentina), and accessibility of the power grid as a supplementary resource. The proposed model integrates solar panels connected to a battery pack by using a double closed-loop control converter. This study emphasises the significance of an empirical analysis of the UDDS load profile to identify daily energy consumption trends and Depth of Discharge (DoD). The model evaluates the system’s overall efficiency, incorporating conversion losses using the Maximum Power Point Tracking (MPPT) algorithm, which maximises solar charging during peak sunlight hours while augmenting with grid power as needed. The most suitable machine learning (ML) algorithm for the observed DoD is predicted and cross-validated using nine selected machine learning approaches and evaluation metrics: Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). ML algorithms examine battery data in conjunction with a comparative analysis of performance indicators. The model simulation evaluated the possibility of solar energy capture, considering the capacity of the converter and its influence on efficient charging capabilities. The employed methodology examines solar energy utilisation alongside grid power augmentation and promotes a sustainable and efficient electric vehicle charging solution.

Lithium-ion batteries (LIBs) are vital energy sources in power systems and daily life, and have relatively high power and energy densities. The expanded use of LIBs has paved the way for advancements in the automobile and energy-storage sectors. Hence, this is a vital transition element for green energy generation in power scenarios. This study investigates the safety aspects in real time and the effects of internal and external parameters on battery performance characteristics. MATLAB/Simulink was used to create an ideal battery model and investigate the effects of several parameters on a standard 18650 Panasonic LIB. In addition, the state of charge (SoC) and State of Health (SoH) were calculated by employing a variety of techniques, including MATLAB/Simulink with an Adaptive Kalman Filter (AKF), in the Real-Time Target Machine (RTTM), and the values were analysed using predictive Machine Learning (ML) algorithms. Temperature and C-rate were the external characteristics examined in this study. The circuit was tested for temperature vectors ranging from −50 °C to 100 °C, and the Urban Dynamometer Driving Schedule (UDDS) drive cycle was set as the load for the emulator and HIL simulations. The percentage errors for the estimated values of the SoC and SoH were also measured. The optimal machine learning algorithm for these parameter estimations was identified in comparison with the seven selected ML models. Evaluation metrics such as the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were used to compare the models.