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identify most degraded cell in serially connected lithium-ion battery pack

since r2022b

syntax

meandifferencemodel(data)
meandifferencemodel(data,name=value)
[worstcell,deltae,deltar0] = meandifferencemodel(___)

description

example

meandifferencemodel(data) analyzes the consistency of the open-circuit voltage (ocv) for each cell in a serially connected lithium-ion battery pack and plots the results. the software combines the recursive least square (rls) algorithm with the mean-difference model (mdm) to estimate the deviation of the ocv of each cell from the mean ocv of the pack [1]. the larger the deviation is in the negative direction, the more likely it is that the battery pack has a fault condition, such as an internal short circuit. the plot legend identifies the cell with the highest deviation, and therefore, the worst cell in the pack.

the algorithm imposes no restrictions on battery activity during the collection of data. the battery can be in any operating sequence, including charging, discharging, and standby phases.

example

meandifferencemodel(data,name=value) incorporates additional options specified by one or more name-value arguments. for example, set the currentvariable argument to 10 to specify that the column with index 10 contains the pack current values.

example

[worstcell,deltae,deltar0] = meandifferencemodel(___) returns the index of the worst cell worstcell, the deviation deltae of the cell voltages from the mean ocv, and the deviation deltar0 of the internal resistances from the mean r0 value. you can use this syntax with any of the previous input-argument combinations.

examples

load the data, which represents an operating profile for an eight-cell battery pack in which one cell is experiencing an internal short circuit. the profile includes standby, driving, charging, and balancing phases. the data consists of 10 columns that contain the sample time (column 1), battery voltages (columns 2–9), and pack current (column 10).

load internalshortcircuit.mat internalshortcircuit

plot the battery voltages and the pack current together.

plot(internalshortcircuit(:,2:9))
hold on
plot(internalshortcircuit(:,10))
legend('voltages (v)','current (a)')
title('internalshortcircuit datasets - voltages and pack current')
ylabel('voltage (v) and current (a) values')
hold off

figure contains an axes object. the axes object with title internalshortcircuit datasets - voltages and pack current, ylabel voltage (v) and current (a) values contains 9 objects of type line. these objects represent voltages (v), current (a).

the voltages appear to track closely, but at this scale it is hard to differentiate them.

zoom into the region after t = 4000 to show the individual voltages more clearly. 

xlim([4000 4010])
ylim([3.4 3.6])
title('voltage separation')
ylabel('voltages (v)')

figure contains an axes object. the axes object with title voltage separation, ylabel voltages (v) contains 9 objects of type line. these objects represent voltages (v), current (a).

the voltages track closely together. there is no cell that is obviously degrading relative to the others.

identify the worst cell using meandifferencemodel.

meandifferencemodel(internalshortcircuit(:,2:end))

figure contains an axes object. the axes object with title estimated ocv deviation from mean, xlabel time, ylabel deltae contains 8 objects of type line. these objects represent 1, 2, 3, 4, 5 (worst cell), 6, 7, 8.

the mean difference model clearly differentiates cell 5.

load the data, which is contained in a table, and show the first two rows.

load internalshortcircuittbl.mat internalshortcircuittbl
head(internalshortcircuittbl,2)
    time    cell1     cell2     cell3    cell4     cell5     cell6     cell7     cell8    current
    ____    ______    ______    _____    ______    ______    ______    ______    _____    _______
     0           0         0        0         0         0         0         0        0       0   
     5      4.0037    4.0018    4.002    4.0012    4.0029    4.0012    4.0042    4.002       0   

data = internalshortcircuittbl;

use meandifferencemodel to identify the worst cell, specifying the time and current variables. use time to specify the time variable. use currentvariable to specify the current variable.

meandifferencemodel(data(:,2:end),time=data.time,currentvariable="current")

figure contains an axes object. the axes object with title estimated ocv deviation from mean, xlabel time, ylabel deltae contains 8 objects of type line. these objects represent cell1, cell2, cell3, cell4, cell5 (worst cell), cell6, cell7, cell8.

the plot identifies the worst cell.

load the data, which is contained in a table.

load internalshortcircuittbl.mat internalshortcircuittbl
data = internalshortcircuittbl;

run meandifferencemodel, using output arguments to store the analysis results.

[worstcell,deltae,deltar0] = meandifferencemodel(data(:,2:end),time=data.time,currentvariable="current");

identify the worst cell.

iwc = worstcell
iwc = 5

plot the estimated internal resistance deviations.

time = data.time;
plot(data.time,deltar0.variables)
title('estimated internal resistance deviation from mean')
legend("cell 1","cell 2","cell 3","cell 4","cell 5","cell 6","cell 7","cell 8")
ylabel('deltar0')

figure contains an axes object. the axes object with title estimated internal resistance deviation from mean, ylabel deltar0 contains 8 objects of type line. these objects represent cell 1, cell 2, cell 3, cell 4, cell 5, cell 6, cell 7, cell 8.

cell 5, which the function identifies as the worst cell, has the largest internal resistance deviation.

input arguments

battery cell voltage and pack current data, specified as a , a , or a matrix that contains ns rows, where ns is the number of samples, and nc 1 columns or variables, where nc is the number of battery cells. by default, the software interprets the last column as the pack current. you can specify a different column for the pack current by using the 'currentvariable' name-value argument.

if your measurement data also includes an explicit column for time, you must exclude that column from the data argument when you call meandifferencemodel. however, you can access the time data for plotting by using the 'time' name-value argument.

example: meandifferencemodel(data(:,2:end),time=data(:,1)) analyzes the ocv deviations and pack current using the voltage and current data in the matrix or table data, and plots the results against the time data in the first column.

name-value arguments

specify optional pairs of arguments as name1=value1,...,namen=valuen, where name is the argument name and value is the corresponding value. name-value arguments must appear after other arguments, but the order of the pairs does not matter.

example: meanvaluemodel(data,currentvariable="current") identifies the table or timetable variable with the name "current" as the pack current variable.

time data to use for plotting, specified as numeric vector, a duration vector, or a datetime vector of length ns, where ns is the number of samples in data. 'time' can be a column in the dataset used for data, or a separate vector.

if data is a timetable, the time information in data takes precedence and the software ignores the 'time' specification.

if data is a table or a matrix and 'time' is not specified, then the function plots the results against the index vector [1:ns].

example: meandifferencemodel(data,time=tv) specifies the time vector tv, which has the same number of rows as data but is separate from data.

pack-current column identifier that provides the variable name or column index of the pack-current data, specified as a string, a character vector, or an integer.

  • when data is a timetable or table, 'currentvariable' can be a string, a character vector, or an integer.

  • when data is a matrix, 'currentvariable' must be an integer.

example: meanvaluemodel(data,currentvariable=10) identifies the column with the index of 10 as the pack current data.

forgetting factor for rls (recursive least squares) estimation, specified as a numeric scalar in the range (0,1]. the value of 'forgettingfactor' determines how significant the past measurements are for parameter estimation. a value of 1 corresponds to "no forgetting", that is, maintaining the values of all past measurements. for more information, see the forgettingfactor argument description in

example: meanvaluemodel(data,forgettingfactor=0.995) sets the forgetting factor to 0.995.

output arguments

index of the worst cell, returned as an integer that corresponds to the column of data that contains the voltage of the cell for which the ocv deviation from the mean ocv is the greatest in the negative direction.

estimated ocv deviations from the mean ocv, returned as a table, timetable, or matrix. the data type of deltae is consistent with the data type of data.

estimated internal resistance deviations from the mean internal resistance r0, returned as a table, timetable, or matrix. the data type of deltar0 is consistent with the data type of data.

references

[1] ouyang, minggao, mingxuan zhang, xuning feng, languang lu, jianqiu li, xiangming he, and yuejiu zheng. “internal short circuit detection for battery pack using equivalent parameter and consistency method.” journal of power sources 294 (october 2015): 272–83. .

extended capabilities

c/c code generation
generate c and c code using matlab® coder™.

version history

introduced in r2022b

see also

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