Li Lingling, Wang Pengchong, Chao Kuei-Hsiang, Zhou Yatong, Xie Yang
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.
PLoS One. 2016 Sep 15;11(9):e0163004. doi: 10.1371/journal.pone.0163004. eCollection 2016.
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.
锂离子电池的剩余使用寿命（RUL）预测与容量退化轨迹密切相关。由于自充电和容量再生，这些轨迹具有多峰性。传统的预测模型，如支持向量机（SVM）或高斯过程回归（GPR），无法准确表征这种多峰性。本文提出了一种基于高斯过程混合（GPM）的新型RUL预测方法。它可以通过分别用不同的GPR模型拟合轨迹的不同段来处理多峰性，从而揭示这些段之间的微小差异。通过对美国国家航空航天局提供的两款商用可充电1850型锂离子电池进行实验，该方法取得了优异的预测结果，证明了其在预测方面的有效性。模型之间的性能比较表明，GPM比SVM和GPR更准确。此外，GPM可以产生预测置信区间，这使得预测比传统模型更可靠。
