EKF电池SOC代码MATLAB

Sure, here is an example of an Extended Kalman Filter (EKF) algorithm for estimating the State of Charge (SOC) of a battery in MATLAB:

function SOC_est = EKF_SOC_estimation(I, V, Ts)
    % Battery model parameters
    R0 = 0.1; % Ohmic resistance
    R1 = 0.01; % Polarization resistance
    C1 = 5000; % Capacitance

    % Initial states and covariance
    SOC0 = 0.5; % Initial SOC estimate
    P0 = 0.1*eye(2); % Initial covariance matrix

    % Process and measurement noise covariance
    Q = diag([0.001, 0.001]); % Process noise covariance
    R = 0.01; % Measurement noise covariance

    % EKF algorithm
    n = length(I);
    SOC_est = zeros(1, n);
    SOC_pred = SOC0;
    P_pred = P0;

    for k = 1:n
        % Prediction step
        SOC_pred = SOC_pred + Ts * (I(k) / C1 - (1 / C1) * SOC_pred);
        A = [-1 / (R1 * C1), 0; 1 / C1, -1 / (R1 * C1)];
        P_pred = A * P_pred * A' + Q;

        % Update step
        H = -1 / R1;
        K = P_pred * H' / (H * P_pred * H' + R);
        SOC_est(k) = SOC_pred + K * (V(k) - H * SOC_pred);
        P_est = (eye(2) - K * H) * P_pred;
        
        % Update prediction and covariance for the next iteration
        SOC_pred = SOC_est(k);
        P_pred = P_est;
    end
end

In this code, `I` and `V` are the measured current and voltage values respectively, and `Ts` is the sampling time interval. The battery model parameters (resistances and capacitance) are provided in the code. The EKF algorithm is implemented with prediction and update steps. The state prediction is based on the battery model, and the state update is performed using the measurement values.

You can use this code as a starting point and modify it according to your specific requirements. Remember to provide valid input data and adjust the battery model parameters based on your battery characteristics.