基于長(zhǎng)短期記憶(LSTM)神經(jīng)網(wǎng)絡(luò)的鋰電池極片面密度精細(xì)預(yù)測(cè)方法研究
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關(guān)鍵詞：鋰電池極片；面密度測(cè)量;X射線測(cè)厚儀；大射線斑；小射線斑擬合;LSTM神經(jīng)網(wǎng)絡(luò)中圖分類(lèi)號(hào)：TM912 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1003-5168（2025）08-0028-06DOI：10.19968/j.cnki.hnkj.1003-5168.2025.08.005
Abstract： [Purposes] The areal density of lithium battery pole piece coating is very important to the capacity，performance and safety of the battery.Especially in the high-speed production line，the measurement accuracy of the coating surface density willdirectly affect the consistency and stability of the battery.However，despite their high resolution，small spot raythickness gauges are expensive and slow to acquire data， making it difficult to meet the needs of large-scale production.Therefore，a fine prediction method of lithium batery pole coating areal density based on Long Short-Term Memory （LSTM） neural network was proposed.[Methods] The LSTM model is trained by the large ray spot measurement data， and the time series characteristics and local variation laws of the areal density data are captured to fit the areal density distribution with small spot resolution，so as to reduce the equipment cost and time consumption under the premise of ensuring the measurement accuracy.[Findings] The experimental results show that the model is applicable to a variety of substrates and samples with defects，especially the fitting effct of the thining area is significantly improved.The model successully achieves the fiting accuracy of the average correlation coeficient of O.999 6，and has a high fitting ability.[Conclusions] This method provides a new idea for the on-line measurement of the coating surface density of lithium battery pole pieces，and has the application potential of achieving both high precision and low cost in highspeed intelligent production lines，and provides effective technical support for production line quality control.
Keywords： lithium battery electrode; areal density measurement; X-ray thickness gauge; large ray spot; small spot fitting;LSTM neural network
0 引言
極片涂布是鋰電池制造過(guò)程中的重要工序，涂布質(zhì)量會(huì)對(duì)鋰電池的容量、一致性和安全性產(chǎn)生重要影響。（剩余4557字）