Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Abstract

It is often difficult for a machine learning model trained based on a small size of charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of lithium-ion (Li-ion) rechargeable batteries. However, in real-world applications, collecting long-term cycling data is a costly and time-consuming process. To overcome this difficulty, we propose a deep learning-based capacity estimation method that incorporates the concepts of transfer learning and ensemble learning. We target the applications where only a relatively small set of training data is available. Transfer learning is a knowledge learning method that leverages the knowledge learned from a source task to improve learning in a related but different target task. Ensemble learning can reduce the risk of choosing a learning algorithm with poor performance by combining prediction results from multiple learning algorithms. In this study, 10-year daily cycling data from eight implantable Li-ion cells is first used as the source dataset to pre-train eight deep convolutional neural network (DCNN) models. The learned parameters of the pre-trained DCNN models are then transferred from the source task to the target task, resulting in eight DCNN with transfer learning (DCNN-TL) models, respectively. These DCNN-TL models are then integrated to build an ensemble model called the DCNN with ensemble learning and transfer learning (DCNN-ETL). The effectiveness of the DCNN-ETL model is verified using a target dataset consisting of 20 commercial 18650 Li-ion cells, and the performance of the model on the target dataset is compared with that of five other data-driven methods including random forest regression, Gaussian process regression, DCNN, DCNN-TL, and DCNN-EL. The verification and comparison results demonstrate that the proposed DCNN-ETL method can produce a higher accuracy and robustness than these other data-driven methods in estimating the capacities of the Li-ion cells in the target task.