Smart Simulation Solutions for Hybrid Solar Engine Performance

Abstract

This work presents the development and validation of a lightweight process simulator capable of reproducing the behavior of a solar-hybrid engine by integrating four computational modules: photovoltaic energy generation, electrochemical energy storage, engine energy supply, and engine operation. The models are solved using a first-order Euler discretization across multiple operating scenarios representative of real hybrid-engine control modes. Model parameters were obtained through time-series fitting of local environmental data, adjustment of solar-panel performance using manufacturer specifications, and targeted experiments isolating the behavior of each subsystem. The simulator accurately reproduced the performance of two hybrid-engine prototypes, an agricultural irrigation system, and an automotive test platform, with correlation coefficients of approximately 0.981 for energy-related variables and 0.926 for chemical species concentrations. The strong agreement between predicted and experimental results demonstrates the robustness of the modeling approach and supports its potential application as an energy-management tool for future solar-hybrid engine systems. To the best of our knowledge, this is the first model in which a chloralkaline process is integrated with solar photovoltaic panels for H2 production and applied in two different scenarios, with the addition of carbon capture within the same procedure.