A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction

As lithium-ion batteries become increasingly popular worldwide, accurately determining their capacity is crucial for various devices that rely on them. Numerous data-driven methods have been applied to evaluate battery-related parameters. In the application of these methods, input features play a critical role. Most researchers often use the same input features to compare the performance of various neural network models. However, because most models are regarded as black-box models, different methods may show different dependencies on specific features given the inherent differences in their internal structures. And the corresponding optimal inputs of different neural network models should be different. Therefore, comparing the differences in optimized input features for different neural networks is essential. This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. The experiment aims to systematically evaluate how changes in health feature types, dimensions, and data volume affect the performance of different methods and find the optimal input for each method. The results demonstrate that each network has its own optimal input in the aspects of health feature types, dimensions, and data volume. Moreover, under the premise of obtaining more accurate prediction accuracy, different networks have different requirements for input data. Therefore, in the process of using different types of neural networks for battery capacity prediction, it is very important to determine the type, dimension, and number of input health features according to the structure, category, and actual application requirements of the network. Different inputs will lead to larger differences in results. The optimization degree of mean absolute error (MAE) can be improved by 10–50%, and other indicators can also be optimized to varying degrees. Therefore, it is very important to optimize the network in a targeted manner.