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 runaway.Accur...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 runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In 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 model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With 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 results.Subsequently,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 261μs.The 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.展开更多
6G通信感知一体化技术作为未来网络发展的核心方向,通过融合通信与感知功能,为构建超高速、低时延、广覆盖的智能互联社会提供支撑。文章聚焦6G通信感知一体化(Integration of Sensing and Communications,ISAC)技术的前沿应用,系统性...6G通信感知一体化技术作为未来网络发展的核心方向,通过融合通信与感知功能,为构建超高速、低时延、广覆盖的智能互联社会提供支撑。文章聚焦6G通信感知一体化(Integration of Sensing and Communications,ISAC)技术的前沿应用,系统性地探讨了自动驾驶汽车集群建模、雷达智能感知算法及端边云协同学习三大核心方向,通过分析比较不同模型的优缺点,旨在研发更智能且适用于未来的6G通信感知一体化技术。展开更多
基金financially supported by the National Natural Science Foundation of China(No.52102470)。
文摘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 runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In 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 model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With 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 results.Subsequently,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 261μs.The 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.
文摘6G通信感知一体化技术作为未来网络发展的核心方向,通过融合通信与感知功能,为构建超高速、低时延、广覆盖的智能互联社会提供支撑。文章聚焦6G通信感知一体化(Integration of Sensing and Communications,ISAC)技术的前沿应用,系统性地探讨了自动驾驶汽车集群建模、雷达智能感知算法及端边云协同学习三大核心方向,通过分析比较不同模型的优缺点,旨在研发更智能且适用于未来的6G通信感知一体化技术。
