基于ADMM模型的插电式混合动力汽车能量管理系统matlab仿真+仿真录像

function [ E, u, time, iterations ] = f_ADMM( coeffs,Pdrv,E0,Pbmin,Pbmax,xmin,xmax,P,C,R,V,misc ) N = length(Pbmin); rho1 = 2.34E-4; rho2 = 1E-8; alpha2 = coeffs(:,1); alpha1 = coeffs(:,2); alpha0 = coeffs(:,3); beta2 = coeffs(:,4); beta1 = coeffs(:,5); beta0 = coeffs(:,6); %% Feasibility check %% Initialization (these operations can be performed offline and are not timed) u = zeros(N,1); u(C) = Pbmin(C); zeta = zeros(N,1); lambda1 = zeros(N,1); lambda2 = zeros(N,1); I = eye(N); Psi = tril(ones(N,N)); M = (rho1/rho2 * inv(Psi) * (inv(Psi)') + I); L = chol(M)'; L = sparse(L); Diff = sparse(inv(Psi)); Difft = sparse(inv(Psi)'); %% Algorithm tic iterations = 0; flag = 1; while flag % The u update for k in P is the solution of a convex optimization % problem. This is solved using a newton method that is included in the % function f_BacktrackingNewtonVector u(P) = f_BacktrackingNewtonVector(alpha0(P), alpha1(P), alpha2(P), beta0(P), beta1(P), beta2(P), V, R, Pdrv(P), rho1, zeta(P), lambda1(P), Pbmin(P), Pbmax(P)); % The x update is trivial x = E0 - cumsum(zeta) - lambda2; x(x > xmax) = xmax; x(x < xmin) = xmin; %hold the current value of zeta for residual calculations zetahold = zeta; % https://arxiv.org/abs/2005.03678 vec = rho1 * (u + lambda1) - rho2 * cumsum(x - E0 + lambda2, 'reverse'); vec = vec / rho2; vec = Difft * vec; vec = Diff * vec; vec = L \ vec; zeta = L' \ vec; % The residual and Lagrange multiplier updates are also trivial r = [u - zeta; x + cumsum(zeta) - E0]; s = [rho1 * (zetahold - zeta); rho2*cumsum(zetahold - zeta)]; lambda1 = lambda1 + (u - zeta); lambda2 = lambda2 + (x + cumsum(zeta) - E0); iterations = iterations + 1; % Termination criteria if iterations > misc.maxIterations flag = 0; end if max(norm(r), norm(s)) < misc.epsilon flag = 0; end end time = toc; E = E0 - cumsum(u); return