Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. (15th May 2023)

• Record Type: Journal Article
• Authors: Yao, Jiachi; Han, Te
• Is Part Of: Energy. Volume 271(2023)
• Publication Date: 2023-05-15
• Subjects: Lithium-ion batteries -- Capacity estimation -- Transfer learning -- Convolutional neural network -- Partial segment; SOH State of health -- ICA Incremental capacity analysis -- GPR Gaussian process regression -- CNN Convolutional neural network -- DTCNN Deep transfer convolutional neural network -- LSTM Long short-term memory -- DBN Deep belief network -- BNN Bayesian neural network -- GCN Graph convolutional network -- MLP Multilayer perceptron -- SVM Support vector machine -- RF Random forest -- ELM Extreme learning machine -- APE Absolute percentage error -- RMSE Root mean squared error -- MAPE Mean absolute percentage error -- MD-MAPE Mean deviation of mean absolute percentage error; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.127033
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate estimation of lithium-ion battery capacity is crucial for ensuring its safety and reliability. While data-driven modelling is a common approach for capacity estimation, obtaining cycling data during charging/discharging processes can be challenging. Collecting cycling data under various charging/discharging protocols is often unrealistic, and the collected data can be fragmented due to the random nature of working conditions in practice. To address these issues, we propose a deep transfer learning method that uses partial segments of charging/discharging data for battery capacity estimation. The proposed method utilizes capacity increment features of partial charging/discharging segments that is designed to satisfy practical scenarios. A deep transfer convolutional neural network (DTCNN) is trained with both source and target data, and a fine-tuning strategy is employed to effectively eliminate distribution discrepancies between different battery types or charging/discharging protocols, leading the improved estimation accuracy. Experimental results demonstrate that the proposed method accurately estimates the lithium-ion battery capacity, with values of RMSE, MAPE, and MD-MAPE of only 0.0220, 0.0247, and 0.0194, respectively, when using partial segments. These results highlight the promising prospects of the proposed method for lithium-ion battery capacity estimation. Highlights: Present a novel deep transfer learning for lithium-ion battery capacity estimation. Capacity increment features are utilized to train the deep learning model. The effectiveness and feasibility of partial charge/discharge data are investigated. Proposed method outperforms other state-of-the-art machine learning models.