The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Co...The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.展开更多
full understanding of the sources of atmospheric nitrous acid(HONO)in the polluted urban atmosphere re-mains a challenge.In this study,ambient HONO and relevant species were measured during January 2019 at an urban si...full understanding of the sources of atmospheric nitrous acid(HONO)in the polluted urban atmosphere re-mains a challenge.In this study,ambient HONO and relevant species were measured during January 2019 at an urban site in Beijing,China,and a budget analysis of HONO was conducted using a box model combined with field observations.Large nighttime“missing sources”of HONO were identified on heavily polluted days based on traditional sources,which had a significant correlation with the relative humidity,ammonia(NH_(3)),and aerosol surface area,and the promotional effect of NH_(3)for nitrogen dioxide(NO_(2))uptake on the wet aerosol surface was discussed.Then,an updated parameterization scheme for quantifying the enhanced heterogeneous reactions of NO_(2)on aerosol surfaces is proposed,and the missing nighttime sources of HONO could be substantially com-pensated after the new scheme was incorporated.Further evaluation on the contributions of HONO to hydroxyl radicals was conducted,and the authors found that the photolysis of HONO played a dominant role in the primary OH production on the polluted days(78%-90%).The study reveals great potential of an NH3-enhanced uptake coefficient of NO_(2)on the aerosol surface in the nocturnal HONO budget,and highlights the significance of HONO in the strong atmospheric oxidation capability during episodes with a heavily polluted atmosphere.展开更多
文摘The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.
基金supported by the National Natural Science Foundation of China[grant numbers 42275120 and 42075111]the National Key Research and Development Program[grant number 2023YFC3706101]。
文摘full understanding of the sources of atmospheric nitrous acid(HONO)in the polluted urban atmosphere re-mains a challenge.In this study,ambient HONO and relevant species were measured during January 2019 at an urban site in Beijing,China,and a budget analysis of HONO was conducted using a box model combined with field observations.Large nighttime“missing sources”of HONO were identified on heavily polluted days based on traditional sources,which had a significant correlation with the relative humidity,ammonia(NH_(3)),and aerosol surface area,and the promotional effect of NH_(3)for nitrogen dioxide(NO_(2))uptake on the wet aerosol surface was discussed.Then,an updated parameterization scheme for quantifying the enhanced heterogeneous reactions of NO_(2)on aerosol surfaces is proposed,and the missing nighttime sources of HONO could be substantially com-pensated after the new scheme was incorporated.Further evaluation on the contributions of HONO to hydroxyl radicals was conducted,and the authors found that the photolysis of HONO played a dominant role in the primary OH production on the polluted days(78%-90%).The study reveals great potential of an NH3-enhanced uptake coefficient of NO_(2)on the aerosol surface in the nocturnal HONO budget,and highlights the significance of HONO in the strong atmospheric oxidation capability during episodes with a heavily polluted atmosphere.
