Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression

Lithium-ion batteries have been widely applied in energy storage systems and electric vehicles (EVs), the remaining useful life (RUL) prediction is one of the critical technologies for prognostics and health management. However, high accuracy RUL prediction with reliability is the biggest bottleneck. To improve RUL prediction and adaptively extract indirect health indicators (HIs), the RUL prediction framework based on the stacked autoencoder and Gaussian mixture regression (SAE-GMR) is proposed. Firstly, the indirect HIs are extracted from charging and discharging data, and the gray relation analysis (GRA) is adopted to analyze the relation with capacity. In this paper, the SAE neural network is proposed to reduce the dimensions and noise of battery and obtain a syncretic HI. Then, the GMR model is estiblished not only to improve accuracy of RUL prediction, but also describe the reliability. Finally, the proposed method is compared with esixting methods,which shows that the proposed model has superiority for other methods.