Remaining useful life prediction of lithium‐ion battery using a novel health indicator,Quality and Reliability Engineering International

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Remaining useful life prediction of lithium‐ion battery using a novel health indicator
Quality and Reliability Engineering International ( IF 2.2 ) Pub Date : 2020-11-07 , DOI: 10.1002/qre.2792
Ranran Wang ₁ , Hailin Feng ₁

Affiliation

Remaining useful life (RUL) prediction plays a significant role in the health prognostic of lithium‐ion batteries (LIBs). The capacity or internal resistance is commonly used to quantify degradation process and predict RUL of LIB, but those two indicators are difficult to be obtained due to complex operational conditions and high costs, respectively. To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized by Box‐Cox transformation and evaluated by correlation analysis for degradation modeling accurately. Finally, relevance vector machine (RVM) algorithm is utilized to make a probabilistic prediction for battery RUL based on the extracted HI. The correlation analysis verifies the effectiveness of the novel HI, and comparative experiments demonstrate the proposed method can predict RUL of LIB more accurately.

中文翻译：

使用新型健康指标预测锂离子电池的剩余使用寿命

剩余使用寿命（RUL）预测在锂离子电池（LIB）的健康预后中起着重要作用。容量或内阻通常用于量化降解过程和预测LIB的RUL，但由于操作条件复杂和成本高，难以获得这两个指标。为了解决这个问题，我们从电池电流曲线中提取了一种新颖的健康指标（HI），可以直接在线对其进行测量。此外，该指标通过Box-Cox转换进行了优化，并通过相关分析进行了评估，以进行准确的降级建模。最后，基于提取的HI，利用相关矢量机（RVM）算法对电池RUL进行概率预测。相关性分析验证了新型HI的有效性，

更新日期：2020-11-07

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