Abstract
Currently, research and applications in the field of capacity prediction mainly focus on the use and recycling of batteries, encompassing topics such as SOH estimation, RUL prediction, and echelon use. However, there is scant research and application based on capacity prediction in the battery manufacturing process. Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method consumes considerable time and energy. To address the above issues, this study establishes an improved extreme learning machine (ELM) model for predicting battery capacity in the manufacturing process, which can save approximately 45% of energy and time in the grading process. The study involves the extraction of features from the battery charge–discharge curve that can reflect battery capacity performance and subsequent calculation of the grey correlation between these features and capacity. The feature set comprises features with a high correlation with capacity, which are used as inputs for the ELM model. Kernel functions are used to adjust the ELM model, and Bayesian optimization methods are employed to automatically optimize the hyperparameters to improve the capacity prediction performance of the model. The study uses lithium-ion battery data from an actual manufacturing process to test the predictive effect of the model. The mean absolute percentage error of the capacity prediction results is less than 0.2%, and the root-mean-square error is less than 0.3 Ah.
