Self-adaptive fuzzy Kalman estimation SOC algorithm

Abstract

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.

Description

Technical field [0001] The present invention relates to a battery management system, and more particularly relates to an adaptive fuzzy Kalman SOC estimation algorithm. Background technique [0002] The state of charge of the battery electric vehicle (State Of Charge, SOC) can be used to characterize the current state of the battery, critical to the operation of the vehicle. BMS (Battery Management System, BMS) is the most critical state of the battery SOC estimate the accuracy of SOC estimation can improve electric vehicle driving range, can also provide effective protection for fault diagnosis battery. SOC estimation when there are safety integration, Kalman filter and neural networks. [0003] Since the Kalman filter is to check the current SOC value at a specific voltage and temperature of the battery open-circuit voltage and the SOC table (OCV-SOC), but since the period of lithium iron phosphate internet OCV-SOC can not effectively open circuit voltage Get value of SOC, the ...

AI Technical Summary

Problems solved by technology

[0003] Since the current Kalman filter is based on the open circuit voltage of the battery and the SOC table (OCV-SOC) to check the SOC value at a specific voltage and temperature, but due to the plateau period of the OCV-SOC of lithium iron phosphate, the open circuit voltage cannot be used effectively. Obtain the value of SOC, so the SOC estimation error of extended Kalman in the plateau period of lithium iron phosphate is relatively large, which may cause SOC jumps before and after the plateau in practical applications, and it is difficult to reflect the true state of SOC

Images