For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing m...For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,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 estimation 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 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.展开更多
Single atom catalysts(SACs) possessing regulated electronic structure, high atom utilization, and superior catalytic efficiency have been studied in almost all fields in recent years. Carbon-based supporting SACs are ...Single atom catalysts(SACs) possessing regulated electronic structure, high atom utilization, and superior catalytic efficiency have been studied in almost all fields in recent years. Carbon-based supporting SACs are becoming popular materials because of their low cost, high electron conductivity, and controllable surface property. At the stage of catalysts preparation, the rational design of active sites is necessary for the substantial improvement of activity of catalysts. To date, the reported design strategies are mainly about synthesis mechanism and synthetic method. The level of understanding of design strategies of carbon-based single atom catalysts is requiring deep to be paved. The design strategies about manufacturing defects and coordination modulation of catalysts are presented. The design strategies are easy to carry out in the process of drawing up preparation routes. The components of carbon-based SACs can be divided into two parts: active site and carbon skeleton. In this review, the manufacture of defects and coordination modulation of two parts are introduced, respectively. The structure features and design strategies from the active sites and carbon skeletons to the overall catalysts are deeply discussed.Then, the structural design of different nano-carbon SACs is introduced systematically. The characterization of active site and carbon skeleton and the detailed mechanism of reaction process are summarized and analyzed. Next, the applications in the field of electrocatalysis for oxygen conversion and hydrogen conversion are illustrated. The relationships between the superior performance and the structure of active sites or carbon skeletons are discussed. Finally, the conclusion of this review and prospects on the abundant space for further promotion in broader fields are depicted. This review highlights the design and preparation thoughts from the parts to the whole. The detailed and systematic discussion will provide useful guidance for design of SACs for readers.展开更多
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
Background The neuroimaging mechanism of major depressive episodes with mixed features(MMF)is not clear.Aims This study aimed to investigate the functional connectivity of the default mode network(DMN)subsystems among...Background The neuroimaging mechanism of major depressive episodes with mixed features(MMF)is not clear.Aims This study aimed to investigate the functional connectivity of the default mode network(DMN)subsystems among patients with MMF and patients with major depressive disorder without mixed features(MDD_(noMF)).Methods This study recruited 47 patients with MDD_(noMF)and 27 patients with MMF from Beijing Anding Hospital,Capital Medical University,between April 2021 and June 2022.Forty-five healthy controls(HCs)were recruited.All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments.Intranetwork and internetwork functional connectity were computed in the DMN core subsystem,dorsal medial prefrontal cortex(dMPFC)subsystem and medial temporal lobe(MTL)subsystem.Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDD_(noMP)MMF and HC groups.Results The functional connectivity within the DMN core(F=6.32,P_(FDR)=0.008)and MTL subsystems(F=4.45,P_(FDR)=0.021)showed significant differences among the MDD_(noMP) MMF and HC groups.Compared with the HC group,the patients with MDD_(noMF) and MMF had increased functional connectivity within the DMN MTL subsystem,and the patients with MMF also showed increased functional connectivity within the DMN core subsystem.Meanwhile,compared with the MDD_(noMP) the patients with MMF had increased functional connectivity within the DMN core subsystem(mean difference(MDD_(noMF)-MMF)=-0.08,SE=0.04,p=0.048).However,no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.Conclusions Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF,findings potentially beneficial to deepen our understanding of MMF's neural basis.展开更多
基金supported in part by the National Natural Science Foundation of China,China(Grant No.52102420)the National Key Research and Development Program of China,China(Grant No.2022YFE0102700)the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。
文摘For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,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 estimation 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 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.
基金funded by the National Natural Science Foundation of China (Nos. 22279118, 31901272, 21401168, U1204203)National Science Fund for Distinguished Young of China (No. 22225202)+1 种基金Young Top Talent Program of Zhongyuan-YingcaiJihua (No. 30602674)Top-Notch Talent Program of Henan Agricultural University (No. 30501034)。
文摘Single atom catalysts(SACs) possessing regulated electronic structure, high atom utilization, and superior catalytic efficiency have been studied in almost all fields in recent years. Carbon-based supporting SACs are becoming popular materials because of their low cost, high electron conductivity, and controllable surface property. At the stage of catalysts preparation, the rational design of active sites is necessary for the substantial improvement of activity of catalysts. To date, the reported design strategies are mainly about synthesis mechanism and synthetic method. The level of understanding of design strategies of carbon-based single atom catalysts is requiring deep to be paved. The design strategies about manufacturing defects and coordination modulation of catalysts are presented. The design strategies are easy to carry out in the process of drawing up preparation routes. The components of carbon-based SACs can be divided into two parts: active site and carbon skeleton. In this review, the manufacture of defects and coordination modulation of two parts are introduced, respectively. The structure features and design strategies from the active sites and carbon skeletons to the overall catalysts are deeply discussed.Then, the structural design of different nano-carbon SACs is introduced systematically. The characterization of active site and carbon skeleton and the detailed mechanism of reaction process are summarized and analyzed. Next, the applications in the field of electrocatalysis for oxygen conversion and hydrogen conversion are illustrated. The relationships between the superior performance and the structure of active sites or carbon skeletons are discussed. Finally, the conclusion of this review and prospects on the abundant space for further promotion in broader fields are depicted. This review highlights the design and preparation thoughts from the parts to the whole. The detailed and systematic discussion will provide useful guidance for design of SACs for readers.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
基金This study was supported by the National Natural Science Foundation of China(81901368,82171526 and 82071531)the Capital's Funds for Health Improvement and Research(CFH2020-4-2125)+1 种基金the Beijing Municipal Administration of Hospitals Incubating Programme(PX2018064 and PX2020072)Beijing Hospitals Authority Youth Programme(QMS20211901).
文摘Background The neuroimaging mechanism of major depressive episodes with mixed features(MMF)is not clear.Aims This study aimed to investigate the functional connectivity of the default mode network(DMN)subsystems among patients with MMF and patients with major depressive disorder without mixed features(MDD_(noMF)).Methods This study recruited 47 patients with MDD_(noMF)and 27 patients with MMF from Beijing Anding Hospital,Capital Medical University,between April 2021 and June 2022.Forty-five healthy controls(HCs)were recruited.All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments.Intranetwork and internetwork functional connectity were computed in the DMN core subsystem,dorsal medial prefrontal cortex(dMPFC)subsystem and medial temporal lobe(MTL)subsystem.Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDD_(noMP)MMF and HC groups.Results The functional connectivity within the DMN core(F=6.32,P_(FDR)=0.008)and MTL subsystems(F=4.45,P_(FDR)=0.021)showed significant differences among the MDD_(noMP) MMF and HC groups.Compared with the HC group,the patients with MDD_(noMF) and MMF had increased functional connectivity within the DMN MTL subsystem,and the patients with MMF also showed increased functional connectivity within the DMN core subsystem.Meanwhile,compared with the MDD_(noMP) the patients with MMF had increased functional connectivity within the DMN core subsystem(mean difference(MDD_(noMF)-MMF)=-0.08,SE=0.04,p=0.048).However,no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.Conclusions Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF,findings potentially beneficial to deepen our understanding of MMF's neural basis.
