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State of health estimation of lithium-ion batteries: A multiscale Gaussian process regression modeling approach

机译：锂离子电池健康状况评估：一种多尺度高斯过程回归建模方法

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摘要

Accurate state of health (SOH) estimation in lithium-ion batteries, which plays a significant role not only in state of charge (SOC) estimation but also in remaining useful life (RUL) prognostics is studied. SOC estimation and RUL prognostics often require one-step-ahead and long-term SOH prediction, respectively. A systematic multiscale Gaussian process regression (GPR) modeling method is proposed to tackle accurate SOH estimation problems. Wavelet analysis method is utilized to decouple global degradation, local regeneration and fluctuations in SOH time series. GPR with the inclusion of time index is utilized to fit the extracted global degradation trend, and GPR with the input of lag vector is designed to recursively predict local regeneration and fluctuations. The proposed method is validated through experimental data from lithium-ion batteries degradation test. Both one-step-ahead and multi-step-ahead SOH prediction performances are thoroughly evaluated. The satisfactory results illustrate that the proposed method outperform GPR models without trend extraction. It is thus indicated that the proposed multiscale GPR modeling method can not only be greatly helpful to both RUL prognostics and SOC estimation for lithium-ion batteries, but also provide a general promising approach to tackle complex time series prediction in health management systems. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 1589-1600, 2015
机译：研究了锂离子电池的准确健康状态（SOH）估计，它不仅在充电状态（SOC）估计中起着重要作用，而且在剩余使用寿命（RUL）预测中也起着重要作用。 SOC估计和RUL预测通常分别需要提前一步和长期SOH预测。提出了一种系统的多尺度高斯过程回归（GPR）建模方法来解决精确的SOH估计问题。利用小波分析方法将SOH时间序列中的整体退化，局部再生和波动解耦。利用包含时间指数的GPR来拟合提取的全局退化趋势，并使用带有滞后矢量输入的GPR来递归地预测局部再生和波动。锂离子电池降解试验的实验数据验证了该方法的有效性。全面评估了提前和多步SOH预测性能。令人满意的结果表明，该方法在没有趋势提取的情况下优于GPR模型。因此表明所提出的多尺度GPR建模方法不仅对锂离子电池的RUL预后和SOC估计都有很大帮助，而且为解决健康管理系统中的复杂时间序列预测提供了通用的有前途的方法。 （c）2015美国化学工程师学会AIChE J，61：1589-1600，2015

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