State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network

• Published in: IEEE access 2020, Vol.8, p.53307-53320
• Main Authors: Zhou, Danhua, Li, Zhanying, Zhu, Jiali, Zhang, Haichuan, Hou, Lin
• Format: Article
• Language: English
• Subjects: Accuracy; Battery charge measurement; Convolution; Feature extraction; Integrated circuit modeling; Life prediction; Lithium; Lithium-ion batteries; Lithium-ion battery; local capacity regeneration; Model accuracy; Monitoring; Noise reduction; Predictive models; Rechargeable batteries; Regeneration; remaining useful life; state of health; temporal convolutional network; Useful life
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2981261

State of health (SOH) monitoring and remaining useful life (RUL) prediction are the key to ensuring the safe use of lithium-ion batteries. However, the commonly used models are inefficient in predicting accuracy and do not have the ability to capture local regeneration of battery cells. In this paper, a temporal convolutional network (TCN) based SOH monitoring model framework of lithium-ion batteries is proposed. Causal convolution and dilated convolution techniques are used in the model to improve the ability of the model to capture local capacity regeneration, thus improving the overall prediction accuracy of the model. Residual connection and dropout technologies are used to improve the training speed of the model and avoid overfitting in deep network. The empirical mode decomposition (EMD) technology is used to denoise the offline data in RUL prediction, so as to avoid RUL prediction errors caused by local regeneration. The proposed model is verified on two kinds of datasets and the results show that it has the ability to capture local regeneration phenomena in Lithium-ion batteries. Compared with the commonly used models, it has higher accuracy and stronger robustness in SOH monitoring and RUL prediction.