Search

The swift expansion of electric vehicle adoption demands significant improvements in durability, safety, and dependability for advanced high-capacity battery systems. Consequently, there is a strong need for data-oriented battery management strategies capable of decoding intricate battery signals and guiding appropriate control of the coupled multi-physical processes inside batteries. At present, battery informatics is gaining traction as an effective strategy, harnessing sophisticated machine learning methods to generate reliable forecasts of battery capability, degradation state, and hazard levels—yet its advancement is severely constrained by insufficient data availability. In response, this work offers one of the pioneering contributions to synthetic and experimental data creation, integrating lab-based measurements with comprehensive three-dimensional (3-D) multi-physics simulations to establish a robust deep learning pipeline accompanied by detailed evaluation of performance metrics and thermal hazard anticipation. In particular, Part I addresses the construction and rigorous verification of the battery simulation framework, conducted in two main phases to confirm its predictive fidelity: initially, validation of the multi-physics approach was performed using measurements from two widely used commercial Li-ion cells—Panasonic NCR18650B and 18650BD; subsequently, an in-depth investigation of the thermal–electrochemical coupling was carried out, yielding valuable model-derived understanding such as temporal voltage profiles and identification of peak local temperature regions (hot spots). This carefully validated model produces high-quality, interpretable datasets suitable for training the hybrid convolutional neural network and long short-term memory architecture (CNN-LSTM), which will be detailed in Part II.

With the rapid expansion of urban power grids and the widespread adoption of renewable energy sources, conventional fault-diagnosis methods for power grids face significant challenges in handling the complexities of modern, highly interconnected systems. While artificial intelligence approaches offer promising alternatives for power grid fault diagnosis, the scarcity of labeled grid data severely hinders the application of AI techniques in this domain. To tackle these issues, this study introduces a semi-supervised learning framework featuring self-supervised learning and an adaptive threshold mechanism (SAT-SSL) designed for fault detection and classification in power grids. Compared with existing approaches, the proposed method substantially reduces reliance on labeled data while maintaining excellent recognition performance. First, a frequency-domain analysis is applied to power grid data to identify and filter anomalous events, which are then classified and labeled based on their visual characteristics, thereby constructing a dedicated power grid dataset. Next, the Yule–Walker algorithm is employed to extract features from the power grid data. A semi-supervised learning framework is subsequently developed, integrating a self-supervised loss and a dynamic threshold mechanism to enhance the model’s information extraction capacity and adaptability across diverse operational scenarios. Finally, the constructed power grid dataset, together with two standard benchmark datasets, is utilized to evaluate the model’s effectiveness. Experimental results demonstrate that the proposed model maintains a low error rate across multiple scenarios and varying label proportions. Specifically, on the power grid dataset, retaining only 5% of the labels yields an error rate of just 6.15%, confirming the method’s ability to achieve precise fault detection and classification even with very limited labeled data.

Swift technological progress has heightened the importance of managing charging demand and refining travel routes for electric vehicle (EV) fleets, particularly in urban settings. Sensors based on IoT, embedded in charging stations and vehicles, supply live information about driver charging routines, road congestion, vehicle positioning, and surrounding conditions—greatly strengthening forecasting and optimization techniques. This continuous stream of IoT data powers the combined deep learning architecture, GNN-ViGNet, to predict upcoming EV charging requirements. An analysis of readings from 400,000 IoT devices distributed across charging points and EVs in Texas was performed to reveal typical charging behavior. These devices continuously log key indicators, including user charging preferences, current traffic levels, and diverse external conditions. Data refinement was achieved through Frequency-Aware Dynamic Range Scaling, together with sophisticated preprocessing techniques including Categorical Encoding. Ultimately, the GNN-ViGNet framework delivered a prediction accuracy of 98.9%. Beyond conventional metrics such as Root-Mean-Square Error (RMSE) and Mean Squared Error (MSE), the research introduced the Forecast Accuracy Rate (FAR) and the Charging Load Variation Index (CLVI) to provide a more comprehensive evaluation of predictive capability. The work introduces both an advanced forecasting system and a novel hybrid Coati–Northern Goshawk Optimization (Coati–NGO) technique designed for route planning. Real-time route modifications become possible thanks to live IoT feeds that include congestion updates, vehicle coordinates, and remaining battery capacity. The Coati–NGO algorithm combines the broad search strength of Coati Optimization (COA) with the refined exploitation strength of Northern Goshawk Optimization (NGO). Compared against Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), it substantially reduced the total distance traveled to 511 km. Moreover, this combined method exhibited quicker convergence, consistently locating high-quality solutions by the 100th iteration. Overall, the presented EV fleet management system leverages continuous IoT inputs to improve charging network performance, reduce operating expenses, and enhance operational effectiveness—delivering an adaptable, realistic framework suitable for city-wide electric mobility.

This paper introduces a novel hybrid artificial intelligence technique designed to enhance energy management efficiency within smart grids, with a particular focus on Renewable Energy Communities (RECs). The proposed approach integrates a Time Delay Neural Network (TDNN) to predict upcoming values of key energy variables in the community. These forecasts then feed into a stochastic Model Predictive Control (MPC) framework, which optimizes community operations through effective Battery Energy Storage System (BESS) control strategies. Evaluation on a public dataset for a 24-hour prediction horizon yielded Mean Absolute Errors (MAE) of 1.60 kW for photovoltaic generation, 2.15 kW for total energy consumption, and 0.30 kW for common services. When integrated with stochastic MPC, these predictions yielded higher financial returns than those from alternative methods. Overall, the system increased total income by 18.72% compared to the same management approach without any forecasting component.

Rooftop solar photovoltaic (PV) systems are playing an increasingly important role in the worldwide shift toward sustainable energy. This development creates a need for precise, high-resolution geospatial assessments of rooftop PV’s technical potential for policy formulation and power system planning. To tackle this issue, we introduce a comprehensive framework that integrates various satellite imagery sources with deep learning techniques to estimate rooftop PV electricity generation. The framework employs a deep learning-based inversion to estimate hourly solar radiation from geostationary satellite imagery. It uses an automated segmentation model to identify building footprints from high-resolution satellite imagery. This enables a detailed assessment of available rooftop areas and hourly simulation of power output at a 100 m spatial resolution. A case study conducted in Jiangsu Province shows that the framework works effectively over large regions and can be scaled from specific sites to broader areas across different time scales. Results indicate that the province’s rooftop resources offer a potential installed capacity of 245.17 GW, which could generate 290.66 TWh of electricity annually. These findings underscore the substantial opportunity for reducing carbon emissions by expanding rooftop PV deployment.