Alkaline water electrolysis (AWE) operated by surplus electricity is suitable for producing green hydrogen in Nepal. Simulation models are built using DWSIM software for AWE, multistage compression, and the Organic Rankine Cycle (ORC). The AWE system's Capital Expenditure (CAPEX) is determined to be $47 million and Operational Expenditure (OPEX) of $7.65 million/year. The storage system, including the multistage compression system and Type IV cylinders, has a CAPEX of $52 million and an OPEX of $17 million/year. The ORC has a CAPEX of $500,000 and an OPEX of $200,000/year. The thermal power generated from AWE and multistage compression can be converted to electricity by the ORC and supplied to the AWE system. This process decreases the Levelized Cost of Hydrogen (LCOH) from $3.5141/kg over 5 years to $3.4725/kg over 25 years. The techno-economic analysis performed confirms the feasibility of implementing these plants in Nepal.

The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each of these was assessed based on its R-squared score, which is an important statistical measure in indicating the variance proportion yielded by the model. In contrast, the Random Forest model significantly improved, with an R-squared value of 0.83, thereby doing an excellent job in capturing nuances of the data. Only surpassed by the Gradient Boosting model at an astonishing R-squared score of 0.87, it is this excellent score that underlines its capability to predict the outcome quite accurately by modeling complex interrelations. In other words, gradient boosting outran the rest and provided the most robust results concerning drivers of students' performance. It also underlines how important choosing a good model is in educational analytics in order to increase the accuracy of the predictions. The use of these models in the proposed EV Battery Charging Cycle Predictor App results in accurate predictions to aid schedule maintenance and energy-related decisions. This research brings light to the future of advanced machine learning methods in enhancing the battery efficiencies of EVs and the development of electric mobility technologies. It is possible that the future work will imply the additional inclusion of real data and the integration of the application to general energy systems.