The estimation of state of charge(SOC)in lithium-ion batteries is important for ensuring the safe and stable operation of battery systems.Under high-rate pulse conditions,the characteristics of short discharge time,hi...The estimation of state of charge(SOC)in lithium-ion batteries is important for ensuring the safe and stable operation of battery systems.Under high-rate pulse conditions,the characteristics of short discharge time,high frequency,large current,strong interference,and complex transient characteristics that make lithium-ion batteries exhibit marked nonlinear characteristics.The existing battery management system has difficulties in capturing the rising and falling edge data of the pulses due to limitations in the sampling frequency.The short idle time makes it challenging to obtain accurate open-circuit voltage,and there are difficulties in identifying the model parameters.Therefore,using a combination of coulomb counting method,open-circuit voltage correction method,and Kalman filtering method to estimate SOC poses certain challenges.This study applies backpropagation neural network(BPNN)combined with Aquila optimizer(AO)algorithm to estimate SOC under high-rate pulse conditions,and experimental verification is performed using special 3-Ah lithium iron phosphate battery.We compared the estimation accuracy of the AO-BPNN model for SOC with the BPNN,support vector machine,extreme learning machine,and Fuzzy neural network models and verified the superiority of AO-BPNN.Furthermore,by utilizing data with larger acquisition intervals,we obtained accurate evaluation results and reduced the data requirements.The effectiveness of the assessment of AO-BPNN was individually verified under different high-rate pulse conditions and different static times through pulse experiments conducted under 9 operating conditions,with the estimation error controlled within 5%.Finally,the robustness of the proposed model was validated using test data with different sampling intervals and random measurement errors.展开更多
Major depressive disorder (MDD) is the most common nonfatal disease burden world-wide. Systemic chronic low-grade inflammation has been reported to be associated with MDD pro-gression by affecting monoaminergic and gl...Major depressive disorder (MDD) is the most common nonfatal disease burden world-wide. Systemic chronic low-grade inflammation has been reported to be associated with MDD pro-gression by affecting monoaminergic and glutamatergic neurotransmission. However,whether various proinflammatory cytokines are abnormally elevated before the first episode of depression is still largely unclear. Here,we evaluated 184 adolescent patients who were experiencing their first episode of depressive disorder,and the same number of healthy individuals was included as con-trols. We tested the serum levels of high-sensitivity C-reactive protein (hs-CRP),tumor necrosis factor-α(TNF-α),IgE,14 different types of food antigen-specific IgG,histamine,homocysteine,S100 calcium-binding protein B,and diamine oxidase. We were not able to find any significant dif-ferences in the serum levels of hs-CRP or TNF-αbetween the two groups. However,the histamine level of the patients (12.35μM) was significantly higher than that of the controls (9.73μM,P<0.001,Mann–Whitney U test). Moreover,significantly higher serum food antigen-specific IgG positive rates were also found in the patient group. Furthermore,over 80% of patients exhibited prolonged food intolerance with elevated levels of serum histamine,leading to hyperpermeability of the blood–brain barrier,which has previously been implicated in the pathogen-esis of MDD. Hence,prolonged high levels of serum histamine could be a risk factor for depressive disorders,and antihistamine release might represent a novel therapeutic strategy for depression treatment.展开更多
基金funded by the National Natural Science Foundation of China(52177206)Joint Fund of the Ministry of Education for Equipment Pre research(8091B022130).
文摘The estimation of state of charge(SOC)in lithium-ion batteries is important for ensuring the safe and stable operation of battery systems.Under high-rate pulse conditions,the characteristics of short discharge time,high frequency,large current,strong interference,and complex transient characteristics that make lithium-ion batteries exhibit marked nonlinear characteristics.The existing battery management system has difficulties in capturing the rising and falling edge data of the pulses due to limitations in the sampling frequency.The short idle time makes it challenging to obtain accurate open-circuit voltage,and there are difficulties in identifying the model parameters.Therefore,using a combination of coulomb counting method,open-circuit voltage correction method,and Kalman filtering method to estimate SOC poses certain challenges.This study applies backpropagation neural network(BPNN)combined with Aquila optimizer(AO)algorithm to estimate SOC under high-rate pulse conditions,and experimental verification is performed using special 3-Ah lithium iron phosphate battery.We compared the estimation accuracy of the AO-BPNN model for SOC with the BPNN,support vector machine,extreme learning machine,and Fuzzy neural network models and verified the superiority of AO-BPNN.Furthermore,by utilizing data with larger acquisition intervals,we obtained accurate evaluation results and reduced the data requirements.The effectiveness of the assessment of AO-BPNN was individually verified under different high-rate pulse conditions and different static times through pulse experiments conducted under 9 operating conditions,with the estimation error controlled within 5%.Finally,the robustness of the proposed model was validated using test data with different sampling intervals and random measurement errors.
基金supported by the Youth Psychological Development Base in China
文摘Major depressive disorder (MDD) is the most common nonfatal disease burden world-wide. Systemic chronic low-grade inflammation has been reported to be associated with MDD pro-gression by affecting monoaminergic and glutamatergic neurotransmission. However,whether various proinflammatory cytokines are abnormally elevated before the first episode of depression is still largely unclear. Here,we evaluated 184 adolescent patients who were experiencing their first episode of depressive disorder,and the same number of healthy individuals was included as con-trols. We tested the serum levels of high-sensitivity C-reactive protein (hs-CRP),tumor necrosis factor-α(TNF-α),IgE,14 different types of food antigen-specific IgG,histamine,homocysteine,S100 calcium-binding protein B,and diamine oxidase. We were not able to find any significant dif-ferences in the serum levels of hs-CRP or TNF-αbetween the two groups. However,the histamine level of the patients (12.35μM) was significantly higher than that of the controls (9.73μM,P<0.001,Mann–Whitney U test). Moreover,significantly higher serum food antigen-specific IgG positive rates were also found in the patient group. Furthermore,over 80% of patients exhibited prolonged food intolerance with elevated levels of serum histamine,leading to hyperpermeability of the blood–brain barrier,which has previously been implicated in the pathogen-esis of MDD. Hence,prolonged high levels of serum histamine could be a risk factor for depressive disorders,and antihistamine release might represent a novel therapeutic strategy for depression treatment.
