Objective:To analyze the impact and mechanism of the SOC model intervention on improving health literacy and reducing disease uncertainty among young and middle-aged patients with coronary heart disease.Methods:A hund...Objective:To analyze the impact and mechanism of the SOC model intervention on improving health literacy and reducing disease uncertainty among young and middle-aged patients with coronary heart disease.Methods:A hundred young and middle-aged patients with coronary heart disease from our hospital between March and October 2024 were randomly divided into an observation group and a control group,with 50 patients in each group.Both groups received routine nursing intervention,while the observation group also received intervention based on the Stages of Change(SOC)model.The intervention period was 30 days.Changes in self-efficacy,health literacy,and disease uncertainty were compared between the two groups before and after the intervention.Results:After the intervention,the self-efficacy scores of both groups increased significantly,and the observation group had higher self-efficacy scores than the control group,with a significant difference(P<0.05).Additionally,the observation group showed significantly higher levels of health literacy than the control group(P<0.001).Furthermore,the observation group had significantly lower scores for disease uncertainty compared to the control group(P<0.001).Conclusion:The SOC model,in addition to routine nursing,significantly affects self-efficacy,disease uncertainty,and health literacy among young and middle-aged patients with coronary heart disease.It helps to enhance patients’knowledge of coronary heart disease,improve health literacy levels,and reduce disease uncertainty,making it worthy of clinical promotion and application.展开更多
针对目前荷电状态(state of charge,SOC)估计方法考虑温度与退化共同影响及其关联耦合关系较少,导致电池性能退化后的模型表征不完善、SOC估计精度不足的问题,提出一种基于退化注入场路耦合模型的锂电池SOC估计方法,以实现全寿命周期下...针对目前荷电状态(state of charge,SOC)估计方法考虑温度与退化共同影响及其关联耦合关系较少,导致电池性能退化后的模型表征不完善、SOC估计精度不足的问题,提出一种基于退化注入场路耦合模型的锂电池SOC估计方法,以实现全寿命周期下SOC的准确估计。首先建立等效电路模型与多物理场模型耦合的场路耦合模型,刻画温度的影响;进而使用离线参数辨识方法将温度、退化等因素注入等效电路模型参数中;最终建立代理模型提高计算效率,实现在线SOC估计。案例验证结果表明,在锂电池经过长时间运行发生退化后,相比于其他方法,所提方法的估计结果具有更平稳的曲线和更高的精度。展开更多
In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distri...In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.展开更多
Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of...Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.展开更多
A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].T...A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.展开更多
在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法...在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法。通过深入研究并网储能系统的拓扑结构与控制策略,构建了细致且精确的数学模型,并运用MATLAB仿真软件进行了建模与分析。实验仿真结果表明,该改进模型能够高效、准确地模拟锂电池SOC的动态变化,为储能系统的优化设计与运行控制提供了理论支持,对于提升储能系统的整体性能具有重要意义。展开更多
准确估计荷电状态(State of Charge,SOC)是确保锂离子电池可靠运行的基础。针对现有深度学习方法输入特征不足的问题,提出一种基于物理模型和深度学习算法的SOC估计方法。该方法结合卷积神经网络(Convolutional Neural Network,CNN)的...准确估计荷电状态(State of Charge,SOC)是确保锂离子电池可靠运行的基础。针对现有深度学习方法输入特征不足的问题,提出一种基于物理模型和深度学习算法的SOC估计方法。该方法结合卷积神经网络(Convolutional Neural Network,CNN)的局部特征提取能力和双向门控循环单元(Bi-directional Gated Recurrent Unit,BiGRU)的时序序列处理能力,通过引入一阶电阻-电容(Resistor-Capacitor,RC)模型输出的端电压作为输入特征,与实测电压、电流共同构成神经网络输入,从而提升CNN-BiGRU对复杂动态工况的建模能力。实验结果表明,CNN-BiGRU模型的SOC估计效果良好,对于马里兰大学高级生命工程中心(Center for Advanced Life Cycle Engineering,CALCE)数据集,常温(25℃)下其均方根误差为0.16%、平均绝对值误差为0.12%。该模型对不同环境温度和不同老化程度的锂电池均具有较高的预测精度和鲁棒性。展开更多
基金Handan City Science and Technology Research and Development Program Project Application(Project No.:23422083096ZC)。
文摘Objective:To analyze the impact and mechanism of the SOC model intervention on improving health literacy and reducing disease uncertainty among young and middle-aged patients with coronary heart disease.Methods:A hundred young and middle-aged patients with coronary heart disease from our hospital between March and October 2024 were randomly divided into an observation group and a control group,with 50 patients in each group.Both groups received routine nursing intervention,while the observation group also received intervention based on the Stages of Change(SOC)model.The intervention period was 30 days.Changes in self-efficacy,health literacy,and disease uncertainty were compared between the two groups before and after the intervention.Results:After the intervention,the self-efficacy scores of both groups increased significantly,and the observation group had higher self-efficacy scores than the control group,with a significant difference(P<0.05).Additionally,the observation group showed significantly higher levels of health literacy than the control group(P<0.001).Furthermore,the observation group had significantly lower scores for disease uncertainty compared to the control group(P<0.001).Conclusion:The SOC model,in addition to routine nursing,significantly affects self-efficacy,disease uncertainty,and health literacy among young and middle-aged patients with coronary heart disease.It helps to enhance patients’knowledge of coronary heart disease,improve health literacy levels,and reduce disease uncertainty,making it worthy of clinical promotion and application.
文摘针对目前荷电状态(state of charge,SOC)估计方法考虑温度与退化共同影响及其关联耦合关系较少,导致电池性能退化后的模型表征不完善、SOC估计精度不足的问题,提出一种基于退化注入场路耦合模型的锂电池SOC估计方法,以实现全寿命周期下SOC的准确估计。首先建立等效电路模型与多物理场模型耦合的场路耦合模型,刻画温度的影响;进而使用离线参数辨识方法将温度、退化等因素注入等效电路模型参数中;最终建立代理模型提高计算效率,实现在线SOC估计。案例验证结果表明,在锂电池经过长时间运行发生退化后,相比于其他方法,所提方法的估计结果具有更平稳的曲线和更高的精度。
基金Sponsored by the National Natural Science Foundation of China (Grant No.50905015)the National High Technology Research and Development Program of China (Grant No.2003AA501800)
文摘In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.
基金Under the auspices of Special Project of National Key Research and Development Program(No.2016YFD0200301)National Natural Science Foundation of China(No.41571206)Special Project of National Science and Technology Basic Work(No.2015FY110700-S2)
文摘Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.
文摘A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.
文摘在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法。通过深入研究并网储能系统的拓扑结构与控制策略,构建了细致且精确的数学模型,并运用MATLAB仿真软件进行了建模与分析。实验仿真结果表明,该改进模型能够高效、准确地模拟锂电池SOC的动态变化,为储能系统的优化设计与运行控制提供了理论支持,对于提升储能系统的整体性能具有重要意义。
文摘准确估计荷电状态(State of Charge,SOC)是确保锂离子电池可靠运行的基础。针对现有深度学习方法输入特征不足的问题,提出一种基于物理模型和深度学习算法的SOC估计方法。该方法结合卷积神经网络(Convolutional Neural Network,CNN)的局部特征提取能力和双向门控循环单元(Bi-directional Gated Recurrent Unit,BiGRU)的时序序列处理能力,通过引入一阶电阻-电容(Resistor-Capacitor,RC)模型输出的端电压作为输入特征,与实测电压、电流共同构成神经网络输入,从而提升CNN-BiGRU对复杂动态工况的建模能力。实验结果表明,CNN-BiGRU模型的SOC估计效果良好,对于马里兰大学高级生命工程中心(Center for Advanced Life Cycle Engineering,CALCE)数据集,常温(25℃)下其均方根误差为0.16%、平均绝对值误差为0.12%。该模型对不同环境温度和不同老化程度的锂电池均具有较高的预测精度和鲁棒性。
