Hybrid approach to battery management: Application of optimization and machine learning for sustainable energy solutions

The paper discusses how optimization methods can be integrated with machine learning models to improve forecasting of battery health and performance over time. With optimisation techniques applied for simplifying feature selection, model tuning, and battery management plan development, there is guaranteed minimal energy loss and a longer lifespan. The machine learning models are some of the regression, classification, and deep learning algorithms applied for the analysis of intricate patterns in operational data to thus allow for an accurate computation of degradation characteristics of batteries such as internal resistance and capacity fading. This study integrates a range of methods to overcome problems associated with data insufficiency, computer power waste, and nonlinearity in degradation. It evaluates the predictive accuracy of sophisticated algorithms including ensemble learning techniques, neural networks, and support vector machines. Optimisation techniques like particle swarm optimization and evolutionary algorithms are being used to find optimal parameters for the model to enhance predictions further. This work which focuses on the collaboration of machine learning and optimization in improving battery technology, is helpful in developing energy systems sustainably.