Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this stu...Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this study introduces a hybrid algorithm observer.This approach combines the proportional-integral(PI)principle with the Kalman filter,utilizing a state-of-charge dynamics model and a current dynamics model.The SOC dynamics model,described by a differential equation,is developed to improve estimation accuracy.Meanwhile,the current dynamics model supports the design of a PI observer,which offers a low-complexity solution for SOC estimation.To address the issue of white noise in measurement signals,a onedimensional Kalman filter is applied.This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer.In addition,the system incorporates parameter observation to estimate key battery parameters.The hybrid observer was tested in a real vehicle to validate its effectiveness.Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.展开更多
基金supported by the Key Research and Development Program of Jiangsu Province(Grant No.BE2021006-2)the Key Science and Technology Program of Anhui Province(Grant No.202423d12050001)+1 种基金the Natural Science Foundation of Anhui Province(Grant No.2308085ME163)the National Natural Science Foundation of China(Grant No.62103415)。
文摘Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this study introduces a hybrid algorithm observer.This approach combines the proportional-integral(PI)principle with the Kalman filter,utilizing a state-of-charge dynamics model and a current dynamics model.The SOC dynamics model,described by a differential equation,is developed to improve estimation accuracy.Meanwhile,the current dynamics model supports the design of a PI observer,which offers a low-complexity solution for SOC estimation.To address the issue of white noise in measurement signals,a onedimensional Kalman filter is applied.This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer.In addition,the system incorporates parameter observation to estimate key battery parameters.The hybrid observer was tested in a real vehicle to validate its effectiveness.Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.
