Battery state of health SOH estimate

SOH (State of Health) estimation is usually used to describe the health state of a battery, that is, the ratio of the battery's current capacity to its initial capacity.

A common SOH estimation method is an experience-based battery life prediction method, including the cycle number method, the ampere-hour method, the weighted ampere-hour method, and the event-oriented aging accumulation method. These methods all use some empirical knowledge in the battery use process to give a rough estimate of battery life based on certain statistical laws. They are only suitable for life prediction in specific situations when the empirical knowledge of battery use is relatively sufficient. .

Another SOH estimation method is the open-loop method based on the durability model. This method describes the increase in solid electrolyte membrane resistance and battery terminal voltage, analyzes the characteristics of the physical and chemical reactions inside the battery, and understands the electrochemical reaction characteristics and The nature of battery capacity fading, thereby directly predicting capacity fading and changes in internal resistance.

T=298.15%25度的绝对温度 

for i=1:9000

% Q(i)=30330*exp(-31500/(8.314*T))*i.^0.552;
Q1(i)=16437*exp((-31700+370.3*3)/(8.314*T))*i.^0.55;  %0.5C
Q2(i)=14892*exp((-31700+370.3*4)/(8.314*T))*i.^0.55;    %2C
Q3(i)=13795*exp((-31700+370.3*5)/(8.314*T))*i.^0.55;    %6C
Q4(i)=12291*exp((-31700+370.3*7)/(8.314*T))*i.^0.55;    %10C

end
figure
plot(100-Q1,'-b','linewidth',2')
hold on
 plot(100-Q2,'-G','linewidth',2)
hold on
plot(100-Q3,'-K','linewidth',2)
hold on
plot(100-Q4,'-r','linewidth',2)
legend('3C','4C','5C','7C','Location','best');
legend('boxoff')


set(gca,'xtick',[0:1000:10000]);
set(gca,'ytick',[0:5:110]);
axis([1 7000 70  101]);



set (gca,'position',[0.16,0.15,0.8,0.8] );
set(gca,'Fontsize',14);


 xlabel('Total Ah throughput （Ah）','FontName','Microsoft YaHei','FontWeight','Bold','FontSize',15)
 ylabel('SOH(%)','FontName','Times New Roman','FontWeight','Bold','FontSize',15)
set(gca,'FontName','Microsoft YaHei','FontWeight','Bold','FontSize',14)

The relationship between B and magnification

C=[3 4 5 7];%倍率
 B=[16437 14892 13795 12291];
 B1=@(i)2.396e+04*abs(i).^(-0.343);
 
 figure
 plot(C,B,'*','linewidth',2)
 hold on
 fplot(@(x)B1(x),[0 11],'r','linewidth',2)
 set(gca,'xtick',[0:1:11]);
 set(gca,'ytick',[0:5000:40000]);
 axis([0 10.5 10000  32001]);
 
 set (gca,'position',[0.16,0.15,0.8,0.8] );
 set(gca,'Fontsize',14);
 
  xlabel('C','FontName','Microsoft YaHei','FontWeight','Bold','FontSize',15)
  ylabel('B','FontName','Times New Roman','FontWeight','Bold','FontSize',15)
 set(gca,'FontName','Microsoft YaHei','FontWeight','Bold','FontSize',14)

Model parameters of simple magnification

C=[3 4 5 7 ];
E=[30589 30218 29848 29107 ];
       p1 =      0.7974
       p2 =      -14.35
       p3 =      -301.6
       p4 =   3.165e+04 
  E1=@(x)p1*x^3+p2*x^2+p3*x+p4

figure
plot(C,E,'*','linewidth',2)
hold on
fplot(@(x)E1(x),[0 11],'r','linewidth',2)
set(gca,'xtick',[0:1:11]);
set(gca,'ytick',[0:1000:50000]);
axis([0 10.5 27000  32001]);

set (gca,'position',[0.16,0.15,0.8,0.8] );
set(gca,'Fontsize',14);

 xlabel('C','FontName','Microsoft YaHei','FontWeight','Bold','FontSize',15)
 ylabel('E(J/mol)','FontName','Times New Roman','FontWeight','Bold','FontSize',15)
set(gca,'FontName','Microsoft YaHei','FontWeight','Bold','FontSize',14)

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