The invention discloses a self-adaptive fuzzy
Kalman estimation SOC
algorithm. The method comprises the following steps: S1, establishing an
equivalent circuit model of a battery, establishing a state-space equation and an observation equation by applying an extended
Kalman algorithm, and estimating a short-time polarization end
voltage variable Vst, a medium-time polarization end
voltage variableVmt, a long-time polarization end
voltage variable Vlt and a battery state-of-charge SOC variable; S2, under the condition that different SOCs are matched with the temperature T, setting equivalent
internal resistance, polarization
capacitance and polarization resistance of an
equivalent circuit model in the charging and discharging process of the battery through a battery characteristic experiment; S3, realizing Kalman prediction and updating, and estimating the SOC value in each sampling period in real time; S4, calculating a corrected
ampere-hour integral factor of the platform period by applying the EKF and the
ampere-hour integral in the OCV-SOC non-platform period; and verifying the corrected
ampere-hour integral of the platform period by applying the EKF again when the platform period is ended, introducing fuzzy control to perform error correction on a platform period correction factor, and finally applying the correction factor to the ampere-hour integral of a new round of non-platform period
correction algorithm. The method has the advantages that the
estimation precision and the
algorithm debugging time of the
algorithm are improved, and the precision of the
extended Kalman filter can meet corresponding requirements by defining parameters in the automatic adjustment method.