Identify most degraded cell in serially connected lithium-ion battery pack
meanDifferenceModel( 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 . 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
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.
returns the index of the worst cell
deltar0] = meanDifferenceModel(___)
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.
Identify Worst Cell in Serially Connected Battery Pack
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
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)')
The voltages track closely together. There is no cell that is obviously degrading relative to the others.
Identify the worst cell using
The mean difference model clearly differentiates cell 5.
Specify Time and Current Variables
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;
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.
The plot identifies the worst cell.
Obtain Model Analysis Results
Load the data, which is contained in a table.
load internalShortCircuitTbl.mat internalShortCircuitTbl data = internalShortCircuitTbl;
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')
Cell 5, which the function identifies as the worst cell, has the largest internal resistance deviation.
data — Battery data
table | timetable | matrix
Battery cell voltage and pack current data, specified as a
timetable, 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.
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.
Specify optional pairs of arguments as
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.
meanValueModel(data,CurrentVariable="Current") identifies the
table or timetable variable with the name
"Current" as the pack current
Time — Time data
numeric vector |
duration vector |
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
'Time' can be a column in the
dataset used for
data, or a separate vector.
data is a
timetable, the time information
data takes precedence and the software ignores the
data is a table or a matrix and
'Time' is not specified, then the function plots the results
against the index vector [1:ns].
meanDifferenceModel(data,Time=tv) specifies the time
tv, which has the same number of rows as
data but is separate from
CurrentVariable — Pack-current column identifier
string | character vector | integer
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.
datais a timetable or table,
'CurrentVariable'can be a string, a character vector, or an integer.
datais a matrix,
'CurrentVariable'must be an integer.
meanValueModel(data,CurrentVariable=10) identifies the
column with the index of
10 as the pack current
ForgettingFactor — Forgetting factor for RLS estimation
1 (default) | numeric scalar in the range (0,1]
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
to "no forgetting", that is, maintaining the values of all past measurements.
more information, see the
ForgettingFactor argument description
meanValueModel(data,ForgettingFactor=0.995) sets the
forgetting factor to 0.995.
worstcell — index of worst cell
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.
deltae — OCV deviations
table | timetable | matrix
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
deltar0 — Internal resistance deviation
table | timetable | matrix
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
 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. https://doi.org/10.1016/j.jpowsour.2015.06.087.
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