首页|Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

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For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand trans-actions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estima-tion method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the △Q with 100 mV voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2％.

Lithium-ion batteryElectric vehiclesHealth estimationFeature extractionConvolutional neural networkDomain adapatation

Zhongwei Deng、Le Xu、Hongao Liu、Xiaosong Hu、Bing Wang、Jingjing Zhou

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School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China

School of Sustainability,Stanford University,Stanford,CA 94305,USA

College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China

China Automotive Engineering Research Institute Co.Ltd.,Chongqing 401122,China

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National Natural Science Foundation of China,ChinaNational Key Research and Development Program of China,ChinaChina Postdoctoral Science Foundation,China

521024202022YFE01027002023T160085

2024

10.1016/j.jechem.2023.10.056

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.89(2)
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