Uncertainty Management in Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Battery
Uncertainty Management in Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Battery
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DOI：
10.1109/tie.2017.2701790
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发表时间：
2017-05
影响因子：
7.7
通讯作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
中科院分区：
文献类型：
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作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
基于Lebesgue采样的故障诊断与预测（LS-FDP）具有计算量小、不确定性积累小的优点。与其他诊断和预后方法一样，LS-FDP的准确性和精确性受到诊断和预后模型中参数和不确定性的显著影响。为了提高LS-FDP的性能，本文提出了一种在线模型参数自适应方案，该方案采用带遗忘因子的递推最小二乘法实现。此外，剩余使用寿命（RUL）预测的不确定性管理通过调整模型噪声通过短期预测和校正循环。为了验证所提出的参数自适应和噪声调整方法，它们被设计和实现在基于粒子滤波的LS-FDP算法中，并应用于锂离子电池。实验结果表明，该方法在电池容量估计和RUL预测方面都有明显的改善。
Lebesgue-sampling-based fault diagnosis and prognosis (LS-FDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the parameters and uncertainties in the diagnostic and prognostic models. To improve performance of LS-FDP, this paper introduces an online model parameter adaptation scheme, which is realized by a recursive least square method with a forgetting factor. In addition, uncertainty of remaining useful life (RUL) prediction is managed by adjusting the model noises through a short-term prediction and correction loop. To verify the proposed parameter adaptation and noise adjustment methods, they are designed and implemented in a particle-filtering-based LS-FDP algorithm with applications to Li-ion batteries. Experimental results show that the proposed approach has significant improvement on both battery capacity estimation and RUL prediction.