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An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction

机译：集成的无味卡尔曼滤波器和相关矢量回归方法，用于锂离子电池的剩余使用寿命和短期容量预测

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摘要

The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regression (RVR) and applied to RUL and short-term capacity prediction of batteries. A RVR model is employed as a nonlinear time-series prediction model to predict the UKF future residuals which otherwise remain zero during the prediction period. Taking the prediction step into account, the predictive value through the RVR method and the latest real residual value constitute the future evolution of the residuals with a time-varying weighting scheme. Next, the future residuals are utilized by UKF to recursively estimate the battery parameters for predicting RUL and short-term capacity. Finally, the performance of the proposed method is validated and compared to other predictors with the experimental data. According to the experimental and analysis results, the proposed approach has high reliability and prediction accuracy, which can be applied to battery monitoring and prognostics, as well as generalized to other prognostic applications. (C) 2015 Elsevier Ltd. All rights reserved.
机译：锂离子电池容量的逐渐降低可以用作跟踪锂离子电池退化的健康指标。重要的是要预测锂离子电池在未来的周期中的容量，以评估其健康状况和剩余使用寿命（RUL）。在本文中，使用无味卡尔曼滤波器（UKF）和相关矢量回归（RVR）开发了一种新方法，并将其应用于电池的RUL和短期容量预测。 RVR模型用作非线性时间序列预测模型，以预测UKF未来残差，否则该残差在预测期间保持为零。考虑到预测步骤，通过RVR方法的预测值和最新的实际残差值会随时变加权方案构成残差的未来演变。接下来，UKF使用将来的残差来递归估计电池参数，以预测RUL和短期容量。最后，对所提方法的性能进行了验证，并与实验数据与其他预测指标进行了比较。根据实验和分析结果，提出的方法具有较高的可靠性和预测精度，可以应用于电池监测和预测，也可以推广到其他预测应用。 （C）2015 Elsevier Ltd.保留所有权利。

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