Method and apparatus for determining state of charge and battery management system

A method for determining a state of charge (SOC) of a battery, comprising:

• Acquiring, by a battery management system (BMS,) state data of the battery, wherein the state data comprises temperature data, SOC data, current data and voltage data;
• determining each element parameter value in an equivalent circuit model of the battery based on the equivalent circuit model, error information, battery characteristic data, and the state data by using a recursive least square (RLS) prediction model, wherein the equivalent circuit model is an RC circuit model;
• the determining each element parameter value in an equivalent circuit model of the battery based on the equivalent circuit model, error information, battery characteristic data, and the state data by using a recursive least square (RLS) prediction model comprises:
• determining, according to the SOC data, a target OCV and a target hysteresis voltage from the OCV-SOC curve and the hysteresis voltage-SOC curve of the battery that correspond to the temperature data;
• and
• determining the element parameter value of the equivalent circuit model of the battery based on the equivalent circuit model, the error information, the current data, the voltage data, the target OCV, and the target hysteresis voltage by using the RLS prediction model;
• and
• determining an estimated value of the SOC of the battery based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data by applying an adaptive unscented Kalman filter (AUKF) observer;
• the determining an estimated value of the SOC of the battery based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data according to a technique of an observer comprises:
• generating a state vector feature point set, a first weight coefficient, and a second weight coefficient according to estimated values of state vectors of the battery at a previous moment and a covariance of the state vectors, wherein the covariance of the state vectors at the previous moment is calculated according to a process noise variance;
• calculating a priori value of a first state vector based on the element parameter value, the state vector feature point set, and the current data by using a first state equation of a state space equation of the battery;
• determining a measurement correction matrix according to the priori value of the first state vector, a measurement noise variance, the temperature data, the current data, the first weight coefficient, the second weight coefficient, and a second output equation of the state space equation of the battery;
• calculating a posteriori value of a second state vector based on the measurement correction matrix, the priori value of the first state vector, and the voltage data;
• and
• determining the estimated value of the SOC of the battery according to the posteriori value of the second state vector and the state space equation of the battery,
• wherein the process noise variance and the measurement noise variance are determined by:
• determining an output residual according to a voltage parameter in the element parameter value and the voltage data;
• calculating an output residual matrix according to a first output residual and the output residual in the estimated value of the SOC of the battery that are calculated at the previous moment;
• determining a theoretical process noise variance and a theoretical measurement noise variance according to the output residual matrix;
• and
• determining the process noise variance and the measurement noise variance based on a noise correction rule according to the theoretical process noise variance and the theoretical measurement noise variance,
• wherein the noise correction rule comprises:
• using the output residual at the previous moment as the process noise variance and the measurement noise variance when a value of the theoretical measurement noise variance is less than a preset threshold;
• and
• using a larger one of an initial value of the process noise variance and a trace of a matrix of the theoretical process noise variance as the process noise variance, and using a larger one of an initial value of the measurement noise variance and the theoretical measurement noise variance as the measurement noise variance, when the value of the theoretical measurement noise variance is not less than the preset threshold.