The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for...The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.展开更多
Perovskite solar cells have attracted considerable attention due to their remarkable efficiency and potential for low-cost production.However,their performance is still impeded by defect states and non-radiative recom...Perovskite solar cells have attracted considerable attention due to their remarkable efficiency and potential for low-cost production.However,their performance is still impeded by defect states and non-radiative recombination.To mitigate this issue,pyromellitic diimide(PD)is employed as an additive to passivate bulk defects in perovskite materials,effectively inhibiting non-radiative recombination and minimizing energy loss within the system.Experimental investigations demonstrate that PD forms hydrogen bonds with formamidinium(FA^(+))ions and coordinates with Pb^(2+) ions,thereby effectively passivating defects.After being treated with PD,the perovskite film exhibits enhanced crystallinity and improved uniformity.As a result of suppressed non-radiative recombination,the solar cell achieves a high open-circuit voltage of 1.193 V along with a power conversion efficiency of 25.79%in 1.55 eV perovskite solar cells.Furthermore,the PDtreated unpackaged device shows improved stability,retaining 96% of its initial efficiency after 2000 h under a nitrogen atmosphere.This study offers valuable insights into developing effective passivation strategies that address defects in perovskite materials.展开更多
基金supported by the Natural Science Foundation of China(No.52377221,62172448)the Natural Science Foundation of Hunan Province,China(No.2023JJ30698)+1 种基金Part of the work is supported by the research project“COBALT-P”(16BZF314C)funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK).Lisen Yan is supported by China Scholarship Council(Grant No.202206370146).
文摘The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.
基金the financial support from the Key Research and Development Program of Ningxia (2025BEE02012)the National Natural Science Foundation of China (52373169)+1 种基金the Research Award Fund for First-class Discipline Construction (Education Discipline) in Higher Education Institutions of Ningxia,China (NXYLXK2021B10)the Ningxia Key Laboratory of Green Catalytic Materials and Technology (2025SYSZD-08)。
文摘Perovskite solar cells have attracted considerable attention due to their remarkable efficiency and potential for low-cost production.However,their performance is still impeded by defect states and non-radiative recombination.To mitigate this issue,pyromellitic diimide(PD)is employed as an additive to passivate bulk defects in perovskite materials,effectively inhibiting non-radiative recombination and minimizing energy loss within the system.Experimental investigations demonstrate that PD forms hydrogen bonds with formamidinium(FA^(+))ions and coordinates with Pb^(2+) ions,thereby effectively passivating defects.After being treated with PD,the perovskite film exhibits enhanced crystallinity and improved uniformity.As a result of suppressed non-radiative recombination,the solar cell achieves a high open-circuit voltage of 1.193 V along with a power conversion efficiency of 25.79%in 1.55 eV perovskite solar cells.Furthermore,the PDtreated unpackaged device shows improved stability,retaining 96% of its initial efficiency after 2000 h under a nitrogen atmosphere.This study offers valuable insights into developing effective passivation strategies that address defects in perovskite materials.
