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Data-driven framework for lithium-ion battery remaining useful life estimation based on improved nonlinear degradation factor

机译：基于改善的非线性降解因子的数据驱动框架剩余的锂离子电池剩余使用寿命估算

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摘要

This paper proposes an improved nonlinear degradation factor based on the current percentage of life-cycle length (CPLL) which contains the battery capacity degradation characteristics information of different periods. This method is improved based on related nonlinear degradation Autoregressive (AR) data-driven prognostics model considering an improved scale nonlinear degradation factor. Then a combination is implemented between the proposed factor and data-driven AR model named nonlinear scale degradation parameter based AR (NSDP-AR) model for better nonlinear prediction ability. Extended Kalman Filter (EKF) is used to obtain the specific factor for certain kind of battery. In order to promote the modified model, a remaining useful life (RUL) prognostic framework using Grey Correlation Analysis (GCA) will be established. The experimental results with the battery data sets from NASA PCoE and CALCE show that the proposed NSDP-AR model and the corresponding prognostic framework can achieve satisfied RUL prediction performance.
机译：本文提出了基于含有不同时期电池容量劣化特性信息的生命周期长度（CPLL）的电流百分比的改善的非线性降解因子。基于相关的非线性劣化自回转性（AR）数据驱动的预后性模型，提高了该方法，考虑了改进的规模非线性降解因子。然后，在所提出的因子和数据驱动的AR模型之间实现了基于非线性比例劣化参数的AR（NSDP-AR）模型的组合，以获得更好的非线性预测能力。扩展卡尔曼滤波器（EKF）用于获得某种电池的特定因素。为了促进修改模型，将建立使用灰色相关分析（GCA）的剩余使用寿命（RUL）预后框架。从NASA PCoE和Calce的电池数据集的实验结果表明，所提出的NSDP-AR模型和相应的预后框架可以实现满足的RUL预测性能。

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