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首页> 外文期刊>Industrial Electronics, IEEE Transactions on >Micro-Short-Circuit Diagnosis for Series-Connected Lithium-Ion Battery Packs Using Mean-Difference Model
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Micro-Short-Circuit Diagnosis for Series-Connected Lithium-Ion Battery Packs Using Mean-Difference Model

机译:基于均值差模型的串联锂离子电池组的微短路诊断

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摘要

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis.
机译:微短路(MSC)是动力电池中的潜在风险,它可能引起热失控甚至灾难性的安全隐患。本文的目的是在初期阶段对MSC进行定量分析,尤其是对于锂离子电池。为了验证该方法的可行性,进行了等效的MSC实验。基于电池单元差异模型,通过扩展卡尔曼滤波器估算电池组的电池单元充电状态(SOC)与平均SOC的差异。评估的SOC差异可以很好地跟踪实际值。此外,通过采用递归最小二乘滤波器开发了一种MSC诊断方法。演示了该方法可准确检查短路电阻。结果还表明,该方法所需的SOC差异和短路电阻诊断所需的计算量较低。

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