基于紅外視頻識(shí)別的鋰電池健康狀態(tài)快速檢測

作者單位:

東華理工大學(xué)機(jī)械與電子工程學(xué)院 南昌 330013

基金項(xiàng)目:

江西省科技合作專項(xiàng)重點(diǎn)項(xiàng)目（20212BDH80008）、國家自然科學(xué)基金(12165001)、科技部常規(guī)性科技援外項(xiàng)目（KY201702002）、江西省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目（20181BBE58006）資助

Rapid detection of lithium battery health status based on infrared video recognition

Author:

Wang Zhicheng

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
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Wang Zhe

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
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Wang Zewang

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
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Zhao Jie

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
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Shu Dengfeng

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
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Affiliation:

School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China

摘要:

針對退役動(dòng)力電池梯次利用過程中對電池健康狀態(tài)快速檢測的需求，本文以軟包磷酸鐵鋰電池為研究對象，提出基于紅外熱成像的鋰電池健康狀態(tài)快速檢測方法。通過改變電池充電和放電電流倍率，研究不同老化程度的電池在放電過程中的溫度變化情況，采集放電過程中的紅外熱成像視頻，建立電池健康狀態(tài)與紅外熱成像特征的對應(yīng)關(guān)系，以此作為電池健康狀態(tài)檢測的健康因子；構(gòu)建基于SlowFast-LSTM深度學(xué)習(xí)網(wǎng)絡(luò)模型的改進(jìn)型視頻識(shí)別算法，對于電池健康狀態(tài)0~40%、40%~50%、50%~60%、60%~70%、70%~80%、80%~100%這6種類別的識(shí)別率達(dá)到80.78%，單次電池檢測時(shí)間3 min，實(shí)現(xiàn)電池健康狀態(tài)的快速檢測。

Abstract:

To meet the demand for rapid detection of battery health status in the process of retired power battery recycling, this paper takes soft pack lithium iron phosphate batteries as the research object and proposes a rapid detection method of lithium battery health status based on infrared thermal imaging. By changing the battery charging and discharging current multipliers, the temperature changes of batteries with different aging degrees during the discharge process are studied, and the infrared thermographic video during the discharge process is collected to establish the correspondence between the battery health state and the infrared thermographic features, which is used as the health factor for battery health state detection; an improved video recognition algorithm based on SlowFast-LSTM deep learning network model is constructed for battery health state detection. The improved video recognition algorithm achieves an average recognition rate of 80.78% for the six categories of battery health state 0~40%, 40%~50%, 50%~60%, 60%~70%, 70%~80% and 80%~100%, and a single battery detection time of 3 minutes, which enables fast detection of battery health state.

汪志成,王哲,王澤旺,趙杰,束登峰.基于紅外視頻識(shí)別的鋰電池健康狀態(tài)快速檢測[J].電子測量技術(shù),2023,46(13):185-192

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