One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

The best solution to exploit renewable energy sources (RES) together is by using the application of microgrid (MG). This indeterminate of resources such as solar panels, wind farm, diesel generator, and battery storage system paired with load profile result in random changes within the generation and the load phases, which create it is challenging to properly manage an MG. Due to this problem energy management technique for real-time scheduling of an MG takes into consideration the uncertainty of load demand, renewable energy, and electricity price are proposed. In this paper, linear programming with two methods (optimized problem and solver based) of dual-simplex are suggested to tackle the energy management issue of the renewable microgrid. The simulation is done in each method using four cases fast wind speed with clear solar irradiance, fast wind speed with cloudy solar irradiance, slow wind speed with clear solar radiation, and slow wind speed with clear solar irradiance. The result showed that the first and second cases in the first method are higher cost than the two corresponding cases in the second method. In addition, the third and fourth cases in the first method are lower cost than the two corresponding cases in the second method.

Green hydrogen is created by electrolysis with electricity derived from sustainable sources such as solar and wind power. Long-distance green hydrogen transportation across numerous mediums can be technically challenging, which has significant effects on the nature of the worldwide market. In this research, techno-economic analysis investigates the cost of green hydrogen transported along 15 regional routes around the world, such as from Egypt to Asia or another continent. Monte Carlo simulation has been proposed to calculate the levelized cost of green hydrogen in various locations over the next few decades. Green hydrogen offers diverse transportation options, ranging from compressed and liquefied forms to energy carriers such as ammonia, methanol, and synthetic fuels. Other methods involve storing hydrogen within materials such as porous carbon, metal hydrides, and even traditional fuels such as diesel, natural gas, and liquefied natural gas. These forms can be transported through a variety of modes, such as shipping, trucking, train transportation, and pipeline transportation. To determine the most cost-effective distribution strategy, a linear programming analysis was conducted for each region. The region’s analysis results in 2030 show that green hydrogen volumes transferred from source regions to destination regions reached 87 Mt.

The research proposes an EMS for grid-connected microgrids which incorporates deep learning predictions, stochastic optimization, and multi-carrier storage systems to optimize cost of operations and greenhouse gas emissions. By jointly reducing operational costs and carbon emissions, the suggested approach will facilitate the transition to sustainable, resilient, low-carbon energy systems. Quick-acting battery storage and slow-reacting hydrogen are used for shaving peaks and balancing energy supply, while decision-making is robustified against the uncertainty associated with renewables through Latin Hypercube Sampling (LHS). The LSTM-based model forecasts 24 h ahead multi-step PV and wind generation outputs. Five algorithms, namely, GA, PSO, GWO, NSGA-II, and the Hippopotamus optimizer (HO), are evaluated for two different microgrid structures (MG1, MG2), using hypervolume metrics and a Wilcoxon rank-sum test conducted on 30 independent runs. The findings indicated that HO provides the best spread and dispersion of the Pareto frontier, along with highest hypervolume, being statistically similar to PSO (p > 0.05), while proving significantly superior to GA and NSGA-II, which get stuck within a highly inefficient region. GWO is overestimated in terms of Pareto convergence, since it is possible at one certain point only. This is impossible in real life, as proven through a peer-to-peer trading counterfactual study. An EMS is capable of allocating renewable excesses into batteries or hydrogen storage and optimizing the profits from grid trading during the 24 h periods. The findings prove HO and PSO algorithms to be reliable energy management solvers for microgrids.