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

By Sheng Shen, Mohammadkazem Sadoughi, Meng Li, Zhengdao Wang, Chao Hu. Applied Energy, 2020

481 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4d93abf7f6bed5567679ee5f6e8e73de75c012c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Zhen Hu
  2. Sankaran Mahadevan
  3. Byeng Dong Youn
  4. Chao Hu

In Automotive Engineering

  1. Zhen Hu
  2. Sankaran Mahadevan
  3. Byeng Dong Youn
  4. Chao Hu

In Electrical and Electronic Engineering

  1. Zhen Hu
  2. Byeng Dong Youn
  3. Chao Hu

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