In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery o...In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.展开更多
Consider the regression model Y i=x τ iβ+g(t i)+ε i for i=1,…, n. Here (x i, t i) are known and nonrandom design points and ε i are i.i.d. random errors.The family of nonparametric estimates n(·) of g(·...Consider the regression model Y i=x τ iβ+g(t i)+ε i for i=1,…, n. Here (x i, t i) are known and nonrandom design points and ε i are i.i.d. random errors.The family of nonparametric estimates n(·) of g(·) including some known estimates is proposed. Based on the model Y i=x τ i+ n(t i)+ε i, the Berry-Esseen bounds of the distribution of the least-squares estimator of β are investigated.展开更多
基金Project(50905015) supported by the National Natural Science Foundation of China
文摘In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.
文摘Consider the regression model Y i=x τ iβ+g(t i)+ε i for i=1,…, n. Here (x i, t i) are known and nonrandom design points and ε i are i.i.d. random errors.The family of nonparametric estimates n(·) of g(·) including some known estimates is proposed. Based on the model Y i=x τ i+ n(t i)+ε i, the Berry-Esseen bounds of the distribution of the least-squares estimator of β are investigated.
