Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization. (1st April 2023)

• Record Type: Journal Article
• Authors: Zhang, Yiwei; Liu, Kexin; Chuang, Yutong; Zhang, Jiusi
• Is Part Of: Measurement science & technology. Volume 34:Number 4(2023)
• Publication Date: 2023-04-01
• Subjects: estimations -- state of charge -- lithium-ion battery -- convolutional neural network-bidirectional long short-term memory network -- transfer learning; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aca115
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: State of charge (SOC) estimation of lithium-ion batteries is of vital significance for the control strategy in battery management systems. To integrate the spatial and temporal characteristics of the data and to accomplish the transfer of knowledge, a novel convolutional neural network-bidirectional long short-term memory network based on transfer learning optimization (CNN-BiLSTM-TF) is proposed to estimate the SOC. Specifically, the spatial and temporal features hidden in the data are learned through CNN and BiLSTM, respectively. Furthermore, the CNN-BiLSTM network is established under one working condition and transferred to other working conditions through transfer learning, from which the SOC can be estimated online. A lithium-ion battery data set is applied to verify the proposed SOC estimation approach. The results of a case study demonstrate that the estimation performance of CNN-BiLSTM-TF is better than some existing approaches.