Forecast-Guided Optimization of Degradation Metrics in Coupled Electrochemical Units

Abstract

The high efficiency and scalability of the coupled electrochemical units, coupled with their operational flexibility, have made them very popular in modern energy storage and conversion systems. Nevertheless, constant operation with a wide range of load requirements and environmental factors is known to accelerate degradation resulting in lower performance, lowered energy efficiency and reduced service life. The currently available degradation management approaches are largely based on reactive control approaches or the static optimization approach; these approaches cannot effectively use future operating information to make proactive decisions. This paper aims to solve this limitation by suggesting a forecast-directed optimization model to combine predictive forecasting with degradation metric optimizations in coupled electrochemical units. The proposed model forecasts future operating situations based on previous measurements of the system and adds the forecasted data into an optimization plan to ensure less degradation and balanced system operation. The methodology is tested in MATLAB/Simulink simulation environment in different operating conditions with different load profiles. Forecast performance is measured by Mean Absolute Error (MAE), Root means square error (RMSE) and mean absolute percent error (MAPE) in order to prove the correctness of prediction. Additional indicators of degradation such as State of Health (SOH), capacity fade and degradation index are used to evaluate the optimization process. The simulation outcomes confirm that the proposed framework delivers more realistic predicting and greatly enhances degradation handling over traditional methods, which boosts the system reliability, functionality, and durability in the long run. These results show that forecast-guided optimization is one of the useful decision support methodologies to be used in intelligent electrochemical energy systems.