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end-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

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Journal of Energy Chemistry 2023年第7期82卷 1-17,I0001页

作者： Bin Ma Lisheng Zhang Hanqing Yu Bosong Zou Wentao Wang Cheng Zhang Shichun Yang Xinhua Liu School of Transportation Science and Engineering Beihang UniversityBeijing 102206China China Software Testing Center Beijing 100038China Institute for Clean Growth and Future Mobility Coventry UniversityCV15FBUnited Kingdom Dyson School of Design Engineering Imperial College LondonLondon SW72AZUK

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal *** estimation and prediction of battery health conditions are crucial for battery safety *** this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven *** on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate *** double exponential decay model is utilized as an empirical model to output highly real-time prediction *** Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time ***,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μ*** proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

关键词： State of health Remaining useful life end-cloud collaboration Ensemble learningDifferential thermal voltammetry

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