the next task to consider is balancing
it is important to keep individual
battery cells roughly at the same state
of charge otherwise the cell with
highest associative L will limit the
amount of charge that we can put into
the pack rendering the system
underutilized this state logic
calculates the voltage difference
between the highest and lowest cell
voltages and based on whether this
difference exceeds a design value
activate pest balancing balance commands
is a boolean vector that indicates which
bleat resistor to activate so that the
cell SOC is slowly reduced doing this
with all cells but the one whose SOC is
the lowest eventually makes all
associates converge within a prescribed
tolerance let's now take a second look
at the simulation results in this
driving charging balancing sequence
example we first observed individual
cell voltages changing as a result of
current flowing in and out at the
beginning of the simulation they are
slightly different because we initialize
the model with a slight associating
balance towards the end of the
simulation the values converge towards
one another as a result of the balancing
procedure and how about currents look at
the charging period during the constant
current stage the current is generated
because the maximum module cell voltage
is high enough compared to a prescribed
four point four volt limit that an
excessively high current could try the
voltage beyond the threshold
significantly limiting the life of the
battery since we calculate the limits
based on the maximum resistance value
within the battery cell lookup table
we're in conservative
a less conservative current-limiting
calculation could use the actual battery
cell resistance at the estimated SOC and
temperature since this information is
available at all operating conditions
the temperature traces show a
significant discrepancy among the
hottest and coldest cell the reason is
mainly be a symmetry in the module
layout in terms of thermal behavior cell
number six gets significantly hotter
than Salama one because it is thermally
insulated on one side even when the
maximum temperature reached during the
simulation is not of immediate concern
in terms of safety the temperature
difference exhibited here will
eventually cause a much faster
degradation of cell 6
compared to cell one leading to an
undesirable unevenness in cell condition
hence the need for active thermal
management to keep thermal differences
within a few degrees Celsius the graph
at the top right shows three SOC
estimation traces of the same battery
cell each performed with a different
method yellow corresponds to cool of
counting blue corresponds to UKF and
orange to EKF the initial SOC the
simulation is seventy-five percent but
the SOC estimators were initialized to
eighty percent to assess their
capability to recover it is apparent
that Coulomb counting never does because
it has no way to realize it is wrong due
to the absence of voltage information
both Kalman filter algorithms on the
other hands recover from the initial
error within the first hour of simulated
time with the EKF slightly outperforming
the UK earth and finally the other two
scopes
indicates that BMS state and each of the
six balanced command signals
respectively in summary we've utilized
Simulink state low sim scape and control
system toolboxes
to design a battery management system
using modeling and simulation