Electric vehicles (EVs) are essential to lowering carbon emissions and solving global environmental issues. The battery powers EVs, making its management crucial to safety and performance. As a self-check system, a Battery Management System (BMS) ensures operating dependability and eliminates catastrophic failures. As batteries age, internal resistance increases and capacity decreases, hence a BMS monitors battery health and performance in real time. EV energy storage systems (ESSs) need a complex BMS algorithm to maintain efficiency. Using battery efficiency calculations that account for charging time, current, and capacity, this approach should reliably forecast the battery's SoC and SoH. As batteries age, internal resistance increases, reducing constant current (CC) charging time. By analyzing these changes, the SoH can be predicted more precisely. Conventional methods for estimating SoC and enhancing BMS performance, such as deep neural networks, are used to minimize error rates. However, as the battery ages, AI approaches have gained prominence for their ability to provide precise diagnostics, fault analysis, and thermal management. These AI-driven techniques significantly enhance safety and reliability during charging and discharging cycles. To further ensure safety, a fault diagnosis algorithm is integrated into the BMS. This algorithm proactively addresses potential issues, thus maintaining the efficiency and safety of the battery. The effectiveness of the proposed BMS algorithms are demonstrated through its successful application in an ESS, validating its capability to manage the battery’s state, enhance performance, and ensure operational sustainability in EVs.

The purpose of this research is to develop an efficient single-phase grid-connected PV system using a better performing asymmetrical multilevel inverter (AMI). Circuit component reduction, harmonic reduction, and grid integration are critical criteria for better inverter efficiency. The proposed inverter’s optimized topology requires seven unidirectional switches, three symmetric dc sources, and three diodes to produce an asymmetric fifteen level output; whereas, the same configuration will generate 7, 11, and 15-level output with an appropriate choice of dc source magnitudes. It is possible to reduce inverter losses and boost efficiency by decreasing the number of switches used. The integration of an asymmetric 15-level inverter with a grid-connected solar photovoltaic system is discussed in this article. A grid-connected solar photovoltaic (GCSPV) system is modelled and simulated using an asymmetric 15-level inverter. The dc sources of the 15-level inverter are replaced with PV sources. The results were analyzed with different operating temperatures and solar irradiance conditions. The GCSPV system is controlled by a closed-loop control system using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), and Hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) based Proportional plus Integral (PI) controllers. Grid voltage, grid current, grid power, and total harmonic distortion (THD) of grid currents were analyzed. The performance of the 15-level asymmetric inverter was evaluated by comparing the THD of the grid current and the efficiency of the grid-connected photovoltaic system.

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications.

These Sustainable Development Goal 11 (SDG 11), whose target is to make urban and rural settlements resilient and disaster-prepared, integrates with it the significant objective of having disaster-resilient communities and cities. GeoDisasterAINet is an AI deep learning and machine learning model for distinguishing between rural and urban-area occurrences of floods, cyclones, earthquakes, and wildfires effectively. In this article, we introduce GeoDisasterAINet. There are four successive phases in model performance improvement, with increasingly enhancing classification accuracy, namely an integrated CNN (baseline model), an integrated CNN with XGBoost (feature improvement model), an integrated CNN with XGBoost and Multiclass SVM (refinement model for decision boundary), and LIME segmentation for interpretability and model performance evaluation.EfficientNetB7, ResNet-50, InceptionV3, and DenseNet-201 are architectures in use in GeoDisasterAINet, and these architectures make three settings for an ensemble: ERI-2025 (EfficientNetB7, ResNet-50, InceptionV3), DRI-2025 (DenseNet-201, ResNet-50, InceptionV3), and DE-2025 (DenseNet-201, EfficientNetB7). In balancing out the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE). With 99.25% accuracy in training, 96.37% accuracy in testing, 98% in precision, 97% in recall, and 97% in F1-score, ERI-2025 + XGBoost + Multiclass SVM, our best model, effectively classified items. LIME-based interpretability uncovered disaster-specific features such as storm behaviour in cyclones, infrastructure loss in earthquakes, wildfire propagation in wildfires, and water build-up in floods, and in confirming that the model was reliable, it helped in an effective location of disasters. GeoDisasterAINet is a strong AI-powered system that addresses directly SDG 11 through its contribution towards safer and disaster-resilient communities and cities. It does this through AI-powered deep learning and machine learning technology for improving disaster detection, evaluation of risk, and preparedness for emergencies in real-time.