Rainbow Electronics MAX17047 - Manual (Page 10)

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MAX17047
ModelGauge m3 Fuel Gauge
Figure 4. ModelGauge m3 Algorithm Mixing Conceptual Illustration
OCV Estimation and Coulomb-Count Mixing
The core of the ModelGauge m3 algorithm is a mixing
algorithm that combines the OCV state estimation with
the coulomb counter. After power-on reset of the IC,
coulomb-count accuracy is unknown. The OCV state
estimation is weighted heavily compared to the coulomb-
count output. As the cell progresses through cycles in
the application, coulomb-counter accuracy improves
and the mixing algorithm alters the weighting so that the
coulomb-counter result is dominant. From this point for-
ward, the IC switches to servo mixing. Servo mixing pro-
vides a fixed magnitude error correction to the coulomb
count, up or down, based on the direction of error from
the OCV estimation. This allows differences between
the coulomb count and OCV estimation to be corrected
quickly. See
Figure 3 .
The resulting output from the mixing algorithm does
not suffer drift from current measurement offset error
and is more stable than a stand-alone OCV estimation
algorithm; see Figure 4 . Initial accuracy depends on the
relaxation state of the cell. The highest initial accuracy is
achieved with a fully relaxed cell.
Fuel-Gauge Empty Compensation
As the temperature and discharge rate of an applica-
tion changes, the amount of charge available to the
applica tion also changes. The ModelGauge m3 algo-
rithm dis tinguishes between remaining capacity of the
cell (RemCap
MIX
) and remaining capacity of the appli-
cation (RemCap
AV
) and reports both results to the user.
Fuel-Gauge Learning
The device periodically makes internal adjustments
to cell characterization and application information to
remove initial error and maintain accuracy as the cell
ages. These adjustments always occur as small under-
corrections to prevent instability of the system and
prevent any noticeable jumps in the fuel-gauge outputs.
Learning occurs automatically without any input from the
host. To maintain learned accuracy through power loss,
the host must periodically save learned information and
then restore after power is returned. See the Power-Up
and Power-On Reset section for details:
• Application Capacity (FullCAP). This is the total
capacity available to the application at full. Through the
user-defined registers, ICHGTerm and FullSOCThr,
the device detects end-of-charge conditions as the
cell is cycled. These points allow the device to learn
the capacity of the cell based on the charge termina-
tion experienced during operation.
Figure 3. ModelGauge m3 OCV and Coulomb-Count Mixing
CELL CYCLES
OCV AND COULOMB-COUNT
MIXING RATIO
1.50 1.00 0.50 0
0%
100%
2.00
COULOMB-COUNT INFLUENCE SERVO MIXING
OCV
INFLUENCE
TIME
STATE-OF-CHARGE ERROR
(SHADED AREA)
ModelGauge m3
OCV + COULOMB-COUNT MIXING
MAXIMUM ERROR RANGE
MAXIMUM COULOMB-COUNTER ERROR
TYPICAL OCV ESTIMATION
ERROR AS CELL IS CYCLED