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3-D Temperature Field Reconstruction for a Lithium-Ion Battery Pack: A Distributed Kalman Filtering Approach

机译:锂离子电池组的3D温度场重构:分布式卡尔曼滤波方法

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Despite the ever-increasing use across different sectors, the lithium-ion batteries (LiBs) have continually seen serious concerns over their thermal vulnerability. The LiB operation involves heat generation and buildup effect, which manifests itself strongly, in the form of highly uneven thermal distribution, for a LiB pack consisting of multiple cells. If not well monitored and managed, the heating may accelerate aging and cause unwanted side reactions. In extreme cases, it will even cause fires and explosions. Toward addressing this threat, this brief, for the first time, seeks to reconstruct the 3-D temperature field of a LiB pack in real time. The major challenge lies in how to acquire a high-fidelity reconstruction with constrained computation time. In this brief, a 3-D thermal model is established first for a LiB pack configured in series, which captures the spatial thermal behavior with a combination of high integrity and low complexity. Given the model, the standard Kalman filter is then distributed to attain temperature field estimation with substantially reduced computational complexity. The arithmetic operation analysis and the numerical simulation illustrate that the proposed distributed estimation achieves a comparable accuracy as the centralized approach but with much less computation.
机译:尽管不同部门的使用量不断增加,但锂离子电池(LiB)仍不断受到对其热脆弱性的严重关注。对于由多个电池组成的LiB电池组,LiB操作涉及热量的产生和积累效应,这种现象以高度不均匀的热分布形式强烈表现出来。如果没有很好的监控和管理,加热可能会加速老化并引起不良的副反应。在极端情况下,它甚至会引起火灾和爆炸。为了解决这一威胁,本摘要首次试图实时重建LiB电池组的3-D温度场。主要挑战在于如何在有限的计算时间下获得高保真度的重建。在本简介中,首先为串联配置的LiB电池组建立了3-D热模型,该模型以高完整性和低复杂性相结合的方式捕获了空间热行为。在给定模型的情况下,然后分发标准的卡尔曼滤波器,从而以显着降低的计算复杂度实现温度场估计。算术运算分析和数值模拟表明,所提出的分布式估计可达到与集中式方法相当的准确性,但计算量却少得多。

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