基于小样本学习的电池多场景阻抗谱在线生成

IF 7.9 2区 综合性期刊 Q1 CHEMISTRY, MULTIDISCIPLINARY Cell Reports Physical Science Pub Date : 2024-07-31 DOI:10.1016/j.xcrp.2024.102134
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引用次数: 0

摘要

车载电化学阻抗谱(EIS)数据采集对于移动电池的状态评估和故障诊断至关重要,但由于测试要求严格、建模数据有限以及不同化学成分电池的机理各不相同,因此在技术上极具挑战性。本文不需要任何额外的传感器,就能将传统的 EIS 测量扩展到在线生成,并涵盖大多数电池使用场景,包括不同的电池化学成分、老化程度、剩余容量和温度。本文采用虚拟仿真和转移技术,利用大幅减少的数据集训练深度神经网络。具体来说,我们使用不超过 24 组数据训练网络,平均相对误差低于 5%,优于大多数涉及 "大数据 "的同类算法。我们的方法降低了车载 EIS 的使用门槛,为实时全面监控电池在时域和频域的性能带来了新的机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Online multi-scenario impedance spectra generation for batteries based on small-sample learning

The onboard acquisition of data from electrochemical impedance spectroscopy (EIS) is critically important to the state assessment and fault diagnosis of mobile batteries, but it is technically challenging due to the stringent test requirements, limited modeling data, and varying mechanisms among batteries with different chemistries. This paper, without requiring any additional sensors, extends the traditional EIS measurement to online generation and covers most battery-using scenarios, including different battery chemistries, aging degrees, remaining capacities, and temperatures. Virtual simulation and transfer techniques are employed to train a deep neural network with a significantly reduced dataset. Specifically, we train the network with no more than 24 groups of data and achieve an average relative error lower than 5%, outperforming most “big data”-involved algorithms of its kind. Our method lowers the threshold of using EIS onboard and unlocks new opportunities to monitor the battery’s performance in both time and frequency domain comprehensively in real time.

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来源期刊
Cell Reports Physical Science
Cell Reports Physical Science Energy-Energy (all)
CiteScore
11.40
自引率
2.20%
发文量
388
审稿时长
62 days
期刊介绍: Cell Reports Physical Science, a premium open-access journal from Cell Press, features high-quality, cutting-edge research spanning the physical sciences. It serves as an open forum fostering collaboration among physical scientists while championing open science principles. Published works must signify significant advancements in fundamental insight or technological applications within fields such as chemistry, physics, materials science, energy science, engineering, and related interdisciplinary studies. In addition to longer articles, the journal considers impactful short-form reports and short reviews covering recent literature in emerging fields. Continually adapting to the evolving open science landscape, the journal reviews its policies to align with community consensus and best practices.
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