MATLAB: Prediction of remaining useful life

Model Predictive Control Toolboxprediction

I am doing my coding to predict the remaining useful life of the battery. I am taking NASA dataset for the predicition. Furthermore i am using charge volatge, current , temperature and capacity as input and target value to be capacity. The problem lies in the prediction of data from a particular set point on x axix. How can we predict the result from a particular point .

Best Answer

Sir , this is my proposed result where the no of cycles are predicted from a particular point on x axix . the graph is between cycle and capacity. Also for your reference i am attaching my code. I have used 4 battery dataset . As told earlier , input are voltage current tempertaure and capacity whereas output is capacity. i trained my nn by utilizing 2 dataset as input and testing the datset from which i wish to acquire results. But i am unable to figure out the prediction as shown in fig attached from 50, 60 and 70 cycles which i wish to perform. 
 
clc
clear all
load B0005
load B0006
load B0007
load B0018
%% load the value of capacity from B0005 B0006 B0007 and B0018
D_cycle1={B0005.cycle.type};
D_cycle2={B0006.cycle.type};
D_cycle3={B0007.cycle.type};
D_cycle4={B0018.cycle.type};
D_cycle1=D_cycle1';
D_cycle2=D_cycle2';
D_cycle3=D_cycle3';
D_cycle4=D_cycle4';
index1 = find(ismember(D_cycle1, 'discharge'))
index2 = find(ismember(D_cycle2, 'discharge'))
index3 = find(ismember(D_cycle3, 'discharge'))
index4 = find(ismember(D_cycle4, 'discharge'))
for i=1:length(index1)
    capacity1(i)=B0005.cycle(index1(i,1)).data.Capacity
end
index=[]
for i=1:length(index2)
    capacity2(i)=B0006.cycle(index2(i,1)).data.Capacity
end
index2=[]
for i=1:length(index3)
    capacity3(i)=B0007.cycle(index3(i,1)).data.Capacity
end
index3=[]
for i=1:length(index4)
    capacity4(i)=B0018.cycle(index4(i,1)).data.Capacity
end
index4=[]
capacity1 = capacity1'
capacity2 = capacity2'
capacity3 = capacity3'
capacity4 = capacity4'
capacity = [capacity1;capacity2;capacity3;capacity4]
%% load the value of voltage current and temperature from B0005 B0006 B0007 and B0018 each cycle(10 samples per cycle)
C_cycle1={B0005.cycle.type};
C_cycle2={B0006.cycle.type};
C_cycle3={B0007.cycle.type};
C_cycle4={B0018.cycle.type};
C_cycle1= C_cycle1'; %%B0005



C_cycle2= C_cycle2'; %%B0006




C_cycle3= C_cycle3'; %%B0007




C_cycle4= C_cycle4'; %%B0018



index5= find(ismember(C_cycle1,'charge')) %%5
index6= find(ismember(C_cycle2,'charge')) %%6
index7= find(ismember(C_cycle3,'charge'))  %%7
index18= find(ismember(C_cycle4,'charge')) %%18
%%B0005
cnt=0
for i=1:length(index5);
    for ii=1:round(length(B0005.cycle(index5(i)).data.Voltage_measured)/10):length(B0005.cycle(index5(i)).data.Voltage_measured);
     cnt=cnt+1   ;
            voltage1(i,cnt)=B0005.cycle(index5(i)).data.Voltage_measured(ii);
            current1(i,cnt)=B0005.cycle(index5(i)).data.Current_measured(ii);
            temperature1(i,cnt)=B0005.cycle(index5(i)).data.Temperature_measured(ii);
            
    end
    cnt=0;
end
index5=[]
%%B0006
for i=1:length(index6);
    for ii=1:round(length(B0006.cycle(index6(i)).data.Voltage_measured)/10):length(B0006.cycle(index6(i)).data.Voltage_measured);
     cnt=cnt+1   ;
            voltage2(i,cnt)=B0006.cycle(index6(i)).data.Voltage_measured(ii);
            current2(i,cnt)=B0006.cycle(index6(i)).data.Current_measured(ii);
            temperature2(i,cnt)=B0006.cycle(index6(i)).data.Temperature_measured(ii);
            
    end
    cnt=0;
end
index=[]
%%B0007
for i=1:length(index7);
    for ii=1:round(length(B0007.cycle(index7(i)).data.Voltage_measured)/10):length(B0005.cycle(index7(i)).data.Voltage_measured);
     cnt=cnt+1   ;
            voltage3(i,cnt)=B0007.cycle(index7(i)).data.Voltage_measured(ii);
            current3(i,cnt)=B0007.cycle(index7(i)).data.Current_measured(ii);
            temperature3(i,cnt)=B0007.cycle(index7(i)).data.Temperature_measured(ii);
            
    end
    cnt=0;
end
index=[]
%%B0018
for i=1:length(index18);
    for ii=1:round(length(B0018.cycle(index18(i)).data.Voltage_measured)/10):length(B0018.cycle(index18(i)).data.Voltage_measured);
     cnt=cnt+1   ;
            voltage4(i,cnt)=B0018.cycle(index18(i)).data.Voltage_measured(ii);
            current4(i,cnt)=B0018.cycle(index18(i)).data.Current_measured(ii);
            temperature4(i,cnt)=B0018.cycle(index18(i)).data.Temperature_measured(ii);
            
    end
    cnt=0;
end
index=[]
%%delete 11th column from B0005 as only 10 samples are required per cycle
voltage1(:,11)=[] 
current1(:,11)=[]
temperature1(:,11)=[]
%%delete 11th column from B0006 as only 10 samples are required per cycle
voltage2(:,11)=[] 
current2(:,11)=[]
temperature2(:,11)=[]
%%delete 11th column from B0007 as only 10 samples are required per cycle
voltage3(:,11)=[] 
current3(:,11)=[]
temperature3(:,11)=[]
%%delete 11th column from B0018 as only 10 samples are required per cycle
voltage4(:,11)=[] 
current4(:,11)=[]
temperature4(:,11)=[]
%% combine the values of voltage current temperature and capacity  
%%B0005   
for vk1=1:size(voltage1,1)
for i = 1 : find(voltage1(vk1,:),1,'last')
   D_voltage1(vk1).voltage1(i)=voltage1(vk1,i)
   D_voltage1(vk1).current1(i)=current1(vk1,i)
   D_voltage1(vk1).temperature1(i)=temperature1(vk1,i)
end
if vk1<=length(capacity1)
    D_voltage1(vk1).capacity1=capacity1(vk1)
else
    continue   
end
end
%%B0006
for vk2=1:size(voltage2,1)
for i = 1 : find(voltage2(vk2,:),1,'last')
   D_voltage2(vk2).voltage2(i)=voltage2(vk2,i)
   D_voltage2(vk2).current2(i)=current1(vk2,i)
   D_voltage2(vk2).temperature2(i)=temperature2(vk2,i)
end
if vk2<=length(capacity2)
    D_voltage2(vk2).capacity2=capacity2(vk2)
else
    continue   
end
end
%%B0007
for vk3=1:size(voltage3,1)
for i = 1 : find(voltage3(vk3,:),1,'last')
   D_voltage3(vk3).voltage3(i)=voltage3(vk3,i)
   D_voltage3(vk3).current3(i)=current3(vk3,i)
   D_voltage3(vk3).temperature3(i)=temperature3(vk3,i)
end
if vk3<=length(capacity3)
    D_voltage3(vk3).capacity3=capacity3(vk3)
else
    continue   
end
end
%% B0018
for vk4=1:size(voltage4,1)
for i = 1 : find(voltage4(vk4,:),1,'last')
   D_voltage4(vk4).voltage4(i)=voltage4(vk4,i)
   D_voltage4(vk4).current4(i)=current4(vk4,i)
   D_voltage4(vk4).temperature4(i)=temperature4(vk4,i)
end
if vk4<=length(capacity4)
    D_voltage4(vk4).capacity4=capacity4(vk4)
else
    continue   
end
end
%%delete 169th and 170th row as data until 168 cycle is required
D_voltage1(169:170) = [];
D_voltage2(169:170) = [];
D_voltage3(169:170) = [];
D_voltage4(133:134) = [];
%%combine the values of voltage current temperature and capacity
%%B0005
for i=1:length(D_voltage1)
input1(i,:)=[D_voltage1(i).voltage1 D_voltage1(i).current1 D_voltage1(i).temperature1 D_voltage1(i).capacity1]
end
%%B0006
for i=1:length(D_voltage2)
input2(i,:)=[D_voltage2(i).voltage2 D_voltage2(i).current2 D_voltage2(i).temperature2 D_voltage2(i).capacity2]
end
%%B0007
for i=1:length(D_voltage3)
input3(i,:)=[D_voltage3(i).voltage3 D_voltage3(i).current3 D_voltage3(i).temperature3 D_voltage3(i).capacity3]
end
%%B0018
for i=1:length(D_voltage4)
input4(i,:)=[D_voltage4(i).voltage4 D_voltage4(i).current4 D_voltage4(i).temperature4 D_voltage4(i).capacity4]
end
input = [ input1; input2; input3; input4 ] %% complete input merged to form one input
output1=capacity1; %%B0005
output2=capacity2; %%B0006
output3=capacity3; %%B0007
output4=capacity4; %%B0018
%% normalize the input
for u=1:size((input),2)
normalize(:,u) = ((input(:,u)-min(input(:,u)))./(max(input(:,u))-min(input(:,u))).*2)-1;
end
input = normalize; 
target = capacity;
%% divide the data
data_2 = input(169:504,:)  %% 2 denotes two batteries for training(B0006 B0007)
data_1 = input(1:168,:)    %% 1 denotes one battery for testing(B0005)
% Separate to training and test data
data_train = input(169:504,:)';
data_test  = input(1:168,:)';
target_train=target(169:504,:)'
target_test=target(1:168,:)
%% BPNN algorithm 
 for pp=1:2
   tic  
net=newff(minmax(data_train),[20 1],{'logsig','purelin'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=1e-6;
net.trainParam.lr= 0.5;
net.performFcn = 'mse';
   
[net, tr] = train(net, data_train, target_train);
capacity_estimated=(sim(net, data_test))';
% 
capacity_error = gsubtract(target_test,capacity_estimated);
RMSE = rms(capacity_error)*100
% MSE = mse(net,target_test,SOC_estimated)
MSE=(mean((capacity_estimated-target_test).^2))*100
MAPE=(sum(abs((capacity_estimated-target_test)./(capacity_estimated)))./length(target_test))*100
MAPE2=(sum(abs((target_test-capacity_estimated)./(capacity_estimated)))./length(target_test))*100
MAE=(sum(abs(capacity_estimated-target_test))./length(target_test))*100
SD=(std(capacity_estimated-target_test))*100
        check1(pp)=RMSE;
        check2(pp)=MSE;
        check3(pp)=MAPE;
        check4(pp)=MAE;
        check5(pp)= SD
        check6(pp,:)=capacity_estimated
        check7(pp,:)=capacity_error
end
[RMSE_BPNN,clm]=min(check1)
MSE_BPNN=min(check2(:,clm))
MAPE_BPNN=min(check3(:,clm))
MAE_BPNN=min(check4(:,clm))
SD_BPNN=min(check5(:,clm))
capacity_estimated_BPNN = check6(clm,:)
capacity_error_BPNN= check7(clm,:)       
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