Abstract
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.
Highlights
As a new type of high energy storage battery, the lithium-ion battery has been widely used in portable electronic devices, electric vehicles and unmanned aerial vehicles due to its fast charging speed, low self-discharge, long life, high energy density and no memory effect [1]
state of health (SOH) monitoring and remaining useful life (RUL) prediction is a key technology in prognostic and health management (PHM)
Effective SOH monitoring is conducive to prolonging battery life, while accurate RUL prediction is an essential means to ensure system security
Summary
As a new type of high energy storage battery, the lithium-ion battery has been widely used in portable electronic devices, electric vehicles and unmanned aerial vehicles due to its fast charging speed, low self-discharge, long life, high energy density and no memory effect [1]. For lithium-ion batteries, safety and reliability are very important in the recycling process. As an important part of the lithium-ion battery management system (BMS), state of health (SOH) essentially reflects the aging and damage of lithium battery. In order to track the aging degree of the battery cell in real time and get the remaining useful life (RUL) of the battery cell. It is necessary to design a method to predict the SOH and RUL of the battery cell effectively, The associate editor coordinating the review of this manuscript and approving it for publication was Yu Liu
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