The invention discloses a low-complexity, high-precision and high-robustness
battery state of charge estimation method and
system, relates to the field of
battery state of charge estimation, and aims to solve the problems of high cost, to-be-improved precision, poor robustness and the like of the existing
battery state of charge estimation technology. According to the technical key points, firstly, a second-order
equivalent circuit model is established through an off-line experiment, an off-line parameter identification means is adopted, the magnitude difference between to-be-identified
model parameters and fitting
voltage is reduced, and the fitting precision is improved; and fitting the relationship between the
model parameters obtained by the off-line parameter identification and the SOC, and fitting an OCV-SOC relationship curve by using the third-order
Gaussian after the fitting points are added. A battery end
voltage and a current value measured by a sensor in real time are input into an ECM model for calculating an OCV value, sliding window filtering of
variable weight distribution is carried out on the calculated OCV value to smooth a curve and reduce errors, a filtered result is fitted through an OCV-SOC relation curve to obtain an SOC value, and the SOC value is used as an observation value of subsequent Kalman filtering. A predicted value is calculated by using an
ampere-hour
integral method, and a current
model parameter is fitted by using the priori value in combination with a relationship between the
model parameter and the SOC. And finally, Kalman filtering is carried out on the two to obtain a posterior SOC value.