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Battery7_RULforStage3_B6.m
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Battery7_RULforStage3_B6.m
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clc; clear; close all
%% Load data-Extract the discharging cycles from Battery 5 and 7
load('data/Data_bettery.mat')
DATA_B7 = DATA_No7; Capacity_B7 = Capacity_No7;
DATA_B5 = DATA_No6; Capacity_B5 = Capacity_No6;
DATA_B5(43) = [];
Phase1 = [1, 30]; Phase2 = [31, 107]; Phase3 = [108, 165];
%% Calculate the minimum length of each phase
[TrainLength3, Trainsample3] = CalLength(DATA_B7, Phase3, 0.7);
[TrainLength1, Trainsample1] = CalLength(DATA_B5, Phase3, 0.7);
TrainLength = min([TrainLength1, TrainLength3]);
Trainsample = min([Trainsample1, Trainsample3]);
%% Obtain the staionary and non-staionary source from training data
tau = 5; m = 3; s = 4;
[TrData_B7, XtrainB7, YS] = Dataslicing1(DATA_B7, Capacity_B7, Phase3, TrainLength, Trainsample, tau, m);
Xtrain = XtrainB7;
[est_Ps, est_Pn, est_As, est_An, ssa_results] = ssa(Xtrain, s, 'reps', 20, 'equal_epochs', Trainsample, 'random_seed', 12345);
Ts = est_Ps * Xtrain; Tn = est_Pn * Xtrain;
[Tn,meanX,stdX] = autoscale_new(Tn');
%% Arranging the training dataset
Xtrain = []; Xtrain_error = [];
for d = Phase3(1):Phase3(1)+Trainsample-1
xtrain = [];
N = size(TrData_B7{d},1);
xtrain = mapminmax('apply', TrData_B7{d}', YS);
xtrain_error = est_Ps * xtrain;
xtrain = est_Pn * xtrain;
for j = 1:9-s
xtrain(j,:) = smoothdata(xtrain(j,:),'gaussian',11);
end
for j = 1:s
xtrain_error(j,:) = smoothdata(xtrain_error(j,:),'gaussian',11);
end
xtrain(:,231:end) = [];
xtrain_error(:,231:end) = [];
xtrain_error(:,1:20) = [];
xtrain = autoscale_new(xtrain',meanX,stdX);
xtrain = xtrain';
N = size(xtrain,2);
cycleID = 1:N;
Capacity = Capacity_B7(d)*ones(N,1);
TrData_B7{d} = [(d-Phase2(1)+1)*ones(N,1) cycleID' xtrain' Capacity];
Xtrain = [Xtrain; TrData_B7{d}];
Xtrain_error{d-Phase2(1)+1} = xtrain_error;
end
%% Arranging Testing Dataset
Xtest = []; TT = []; Xtest_D = []; Xtest_Error = [];
for d = Phase3(1):Phase3(2)
xtest = [];
N = size(DATA_B5{d},2);
data = DATA_B5{d}(:,1:TrainLength);
N = size(data, 2);
data = [reconstitution(data(1,:), N, m, tau); reconstitution(data(2,:), N, m, tau); reconstitution(data(3,:), N, m, tau)];
data = mapminmax('apply', data, YS);
xtest_error = est_Ps * data;
xtest = est_Pn * data;
for j = 1:9-s
xtest(j,:) = smoothdata(xtest(j,:),'gaussian',11);
end
for j = 1:s
xtest_error(j,:) = smoothdata(xtest_error(j,:),'gaussian',11);
end
xtest(:,231:end) = [];
xtest_error(:,231:end) = [];
xtest_error(:,1:20) = [];
xtest = autoscale_new(xtest',meanX,stdX);
xtest = xtest';
cycleID = 1:size(xtest,2);
Xtest{d-Phase3(1)+1} = [(d-Phase3(1)+1)*ones(size(xtest,2),1) cycleID' xtest'];
Xtest_D = [Xtest_D; Xtest{d-Phase3(1)+1}];
TT = [TT xtest];
Xtest_Error{d-Phase3(1)+1} = xtest_error;
end
%% Arrange the data for the following LSTM
for d = Phase3(1):Phase3(1)+Trainsample-1
TrData_B7{d}(:, 1:2) = [];
TrData_B7{d}(:, end) = [];
TrData_B7{d} = TrData_B7{d}';
end
TrData = TrData_B7(Phase3(1):Phase3(1)+Trainsample-1);
for d = 1: Phase3(2)-Phase3(1)+1
Xtest{d}(:, 1:2) = [];
Xtest{d} = Xtest{d}';
end
X_training = TrData; Y_training = Capacity_No7(Phase3(1):Phase3(1)+Trainsample-1);
X_validate = Xtest; Y_validate = Capacity_No5(Phase3(1):Phase3(2));
%% Train the model
inputsize = 9-s;
numHiddenUnits1 = 100; numHiddenUnits2 = 100; numResponses = 1;
layers = [ ...
sequenceInputLayer(inputsize)
lstmLayer(numHiddenUnits1, 'OutputMode', 'sequence')
lstmLayer(numHiddenUnits2, 'OutputMode', 'last')
fullyConnectedLayer(50)
fullyConnectedLayer(numResponses)
regressionLayer];
maxEpochs = 200; miniBatchSize = 4;
options = trainingOptions('adam', ...
'MaxEpochs', maxEpochs, ...
'MiniBatchSize', miniBatchSize, ...
'InitialLearnRate', 0.01, ...
'GradientThreshold', 1, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress',...
'Verbose', 0);
net = trainNetwork(X_training, Y_training', layers, options);
%% Plot the training data
YPred_train =[]; Ytrain = [];
for i = 1 : size(X_training, 2)
yPred_train = predict(net, X_training{i}, 'MiniBatchSize',1);
YPred_train = [YPred_train yPred_train];
Ytrain = [Ytrain Y_training(i)];
end
YPred_validate = []; Yvalidate = [];
for i = 1 : size(X_validate, 2)
yPred_validate = predict(net, X_validate{i}, 'MiniBatchSize',1);
YPred_validate = [YPred_validate yPred_validate];
Yvalidate = [Yvalidate Y_validate(i)];
end
%%
CorrectL = 20;
TrainError = YPred_validate(1:CorrectL) - Yvalidate(1:CorrectL);
inputsize_error = 9-s;
numHiddenUnits_error = 20; numResponses = 1;
layers_error = [ ...
sequenceInputLayer(inputsize_error)
lstmLayer(numHiddenUnits_error,'OutputMode','last')
fullyConnectedLayer(50)
fullyConnectedLayer(numResponses)
regressionLayer];
maxEpochs_error = 50; miniBatchSize = 2;
options_error = trainingOptions('adam', ...
'MaxEpochs', maxEpochs_error, ...
'MiniBatchSize', miniBatchSize, ...
'InitialLearnRate', 0.01, ...
'GradientThreshold',1, ...
'Shuffle','every-epoch', ...
'Plots','training-progress',...
'Verbose',0);
net_error = trainNetwork(X_validate(1:CorrectL), TrainError', layers_error, options_error);
YPred_validateError = []; YvalidateError = [];
for i = 1 : size(X_validate, 2)
yPred_validate_error = predict(net_error, X_validate{i}, 'MiniBatchSize',1);
YPred_validateError = [YPred_validateError yPred_validate_error];
end
%% Plot the concerned information for training dataset
figure
subplot(121)
plot(YPred_train, 'o-', 'Linewidth', 1.5, 'MarkerSize', 4);
hold on;
plot(Ytrain, 'h--', 'MarkerFaceColor', 'r');
xlabel("No. of training cycles");
ylabel("Capacity (Amp-hr)");
legend('Prediction (Proposed)', 'Real data');
xlim([0, size(Ytrain, 2)]);
subplot(122)
bar(YPred_train-Ytrain)
xlabel("No. of training cycles")
ylabel("Error (Amp-hr)")
xlim([0, size(Ytrain, 2)])
%% Plot the concerned information for testing dataset
K = 1:size(Yvalidate, 2);
figure
subplot(121)
plot(YPred_validate, 'o-','Linewidth', 1.5, 'MarkerSize',4)
hold on
plot(Yvalidate, 'h--', 'MarkerFaceColor', 'r')
xlabel("No. of testing cycles")
ylabel("Capacity (Amp-hr)");
legend('Prediction (Proposed)','Real data');
xlim([0, size(Yvalidate, 2)])
subplot(122)
bar(YPred_validate-Yvalidate)
xlabel("No. of testing cycles")
ylabel("Error (Amp-hr)")
xlim([0, size(Yvalidate, 2)])
%% Calculate the estimation error
m = mean(YPred_validate-Yvalidate);
s = std(YPred_validate-Yvalidate);
RMSE = sqrt(mean((YPred_validate-Yvalidate).^2))/2 * 100
YPred_validate = YPred_validate - YPred_validateError;
%% Plot the concerned information for testing dataset
K = 1:size(Yvalidate, 2);
figure
subplot(121)
plot(YPred_validate, 'o-','Linewidth', 1.5, 'MarkerSize',4)
hold on
plot(Yvalidate, 'h--', 'MarkerFaceColor', 'r')
xlabel("No. of testing cycles")
ylabel("Capacity (Amp-hr)");
legend('Prediction (Proposed)','Real data');
xlim([0, size(Yvalidate, 2)])
subplot(122)
bar(YPred_validate-Yvalidate)
xlabel("No. of testing cycles")
ylabel("Error (Amp-hr)")
xlim([0, size(Yvalidate, 2)])
%% Calculate the estimation error
m = mean(YPred_validate-Yvalidate);
s = std(YPred_validate-Yvalidate);
RMSE = sqrt(mean((YPred_validate-Yvalidate).^2))/2 * 100