title: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture.
abstract: The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models.
authors.affiliation: Province-ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, 300130, China.|College of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung, 41170, Taiwan.
authors.forename: Lingling
authors.initials: L
authors.lastname: Li
authors.affiliation: Province-ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, 300130, China.
authors.forename: Pengchong
authors.initials: P
authors.lastname: Wang
authors.affiliation: College of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung, 41170, Taiwan.
authors.forename: Kuei-Hsiang
authors.initials: KH
authors.lastname: Chao
authors.affiliation: School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300130, China.
authors.forename: Yatong
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Province-ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, 300130, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Xie
mesh_terms: D011211:Electric Power Supplies; D008094:Lithium; D008962:Models, Theoretical
chemical_list: D008094:Lithium