Gauging Algorithm Comparisons · 2016. 10. 10. · m := cell mass . c. p:= specific heat . h. c :=...

TI Information – Selective Disclosure 2010 Dallas BMS Deep Dive 2011 Dallas Deep Dive 1

Gauging Algorithm Comparisons

TI Information – Selective Disclosure 2010 Dallas BMS Deep Dive 2011 Dallas Deep Dive

Agenda • Basic Vocabulary Review

• How to estimate battery capacity? – Voltage lookup – Current integration

• Factors affecting capacity estimation (Or, why you may not want to try this at home!)

• Deeper dive into CEDV – Learning Before Fully Discharged – Learning Before Fully Discharged with CEDV – CEDV Model Visualization

• Deeper dive into Impedance Track – Current Direction and Relaxation mode – OCV profiles – Resistance learning – Qmax learning – FCC and Remaining Capacity simulation

• IT-DVC


Basic Vocabulary Review • Capacity

– Design Capacity [mAh] – Qmax, Chemical Capacity [mAh] – FCC, Usable Capacity [mAh] – RM, Remaining Capacity [mAh] – RSOC [%] – DOD [%] – DOD0 [%]

• Voltages – OCV [mV] – OCV(DOD) [mV] – EDV [mV] – EDV 2 [mV] – EDV 0 [mV] – CEDV [mV]

• Current – C-rate [mA] – Coulomb Counting

∫ ⋅+= dttIqtq )()( 0


• External battery voltage (blue curve) V = V0CV – I • RBAT • Higher C-rate EDV is reached earlier (higher I • RBAT)

EDV

Full chemical capacity: Qmax Usable capacity : FCC

0 1 2 3 4 6

3.0

3.5

4.0

4.5

Capacity, Ah

Voltage, V

How Much Capacity is Really Available?

Open circuit voltage (OCV)

I • RBAT


How to estimate battery capacity?

• Measure change in capacity –Voltage lookup –Coulomb counting –Combination of both

• Develop a cell model –Circuit model –Table Lookup


Voltage lookup

)(tq

mL marks

V(t)

I(t)

• One can tell how much water is in a glass by reading the water level – Accurate water level reading should

only be made after the water settles (no ripple, etc)

• One can tell how much charge is in a battery by reading well-rested cell voltage – Accurate voltage should only be made

after the battery is well rested (stops charging or discharging)


OCV curve

Level rises same rate

Level rises same rate

Volta

ge

Fullness

OCV Curve Full charge voltage

End of discharge voltage

0% 100% Capacitor

Level rises faster

Level rises slower

Volta

ge

Fullness

OCV Curve Full charge voltage

End of discharge voltage

0% 100% Battery


OCV voltage table: DOD representation

OCV(DOD)

2900

3100

3300

3500

3700

3900

4100

4300

0 0.2 0.4 0.6 0.8 1 1.2

DOD

Volta

ge_a

(DO

D)

Voltage_a

Poly_a(DOD)

Flat Zone

Vmax Vmin

DOD = Depth of Discharge SOC = State of Charge DOD = 100% - SOC


Current integration

• One can also measure how much water goes in and out

• In batteries, battery capacity changes can be monitored by tracking the amount of electrical charges going in/out

• But how do you know the amount of charge, , already in the battery at the start?

• How do you count charges accurately? )(tq

mL marks

Voltage

I(t) ∫ ⋅+= dttIqtq )()( 0

∑⋅∆+=k kk Itqq 0

0q


Basic Smart Battery System

Batte

ry Mo

del

Gas Gauge

Rs Ibatt

Vbatt

Load

Char

ger

VCHG

ICHG

comm

CHG DSG

IDSG

VDSG

VPACK

Tbatt

∑⋅∆+=k kk Itqq 0


Circuit model • VOC a function of SOC

• Rint is internal resistance

• Rs and Cs model the short term transient response

• RL and CL model the long term transient response

• Vbatt and Ibatt are the battery voltage and current

• All parameters are function of temperature and battery age

Voc(SOC)

Rint RL

CS CL

RS Vbatt

Ibatt

DC model

Voc(SOC)

Rint RL

CS CL

RS Vbatt

Ibatt

Transient model


Table lookup

• Large, multi-dimensional table relating capacity to – Voltage – Current – Temperature – Aging

• No cell model

• Apply linear interpolation to make lookup “continuous”

• Memory intensive


Factors affecting capacity estimation

• PCB component accuracy

• Instrumentation accuracy

• Cell model fidelity

• Aging

• Temperature


PCB component accuracy

• Example – Current sensing resistor – Trace length (resistance)

sRtItV ⋅= )()(

Gas Gauge

Rs )(tI

rsRtVtIε±

=)()(

R+ R-


Instrumentation accuracy

ADC count

Volta

ge

• ADC Resolution

• Sampling rate

• Voltage drift / calibration

• Noisy immunity


Battery model fidelity

• Steady-state (DC)

• Transient (AC)

• Capacity degradation – Aging – Overcharge

Voc(SOC)

Rint RL

CS CL

RS Vbatt

Ibatt

Transient model

Voc(SOC)

Rint RL

CS CL

RS Vbatt

Ibatt

DC model


Model parameter extraction

• Extract battery model parameter values using actual collected battery data – Open circuit voltage (OCV) – Transient parameters (RC) – DC parameters (Ri)

• Least square minimization

• Extraction process can be hard and time consuming


Temperature

• Temperature is important for – Capacity estimation – Safety – Charging control

• Temperature impacts model parameters – Resistance – Capacitance – OCV – Max capacity


Modeling temperature

Voc(Vsoc)

R i

C

R

Vbatt

Ibatt

[ ] ( )acibattocbattibattp TTAhRIVVR

RIdtTdcm −−−−+= 22 1

m := cell mass cp := specific heat

hc := heat transfer coef A := cell surface area

Ta := ambient temp

Heating Cooling

• Based on a heating / cooling model **

• Heating is from the internal resistance

• Cooling is from heat transfer to the environment, i.e.,

• How many thermistors?

** “Dynamic Lithium-Ion Battery Model for System Simulation”, L. Gao, S. Liu and R. A. Dougal, IEEE Transaction on Components and Packaging Technologies, vol. 25, no. 3, September 2002.

( )aTT −


What Does A Fuel Gauge Do?

3V

4.2V

Which route is the battery taking?

Suppose we are here

0%

• What is the remaining capacity at current load?

• What is the State of charge (SOC)?

• How long can the battery run?


Current Integration Based Fuel-gauging • Battery is fully charged

• During discharge capacity is integrated

• State of charge (SOC) at each moment is RM/FCC

• FCC is updated every time full discharge occurs

0% 3V RM = FCC - Q

SOC = RM/FCC

4.2V

Q

FCC


Learning Before Fully Discharged – fixed voltage thresholds

7% 3%

0% EDV0

FCC

4.2V

• It is too late to learn when 0% capacity is reached Learning FCC before 0%

• We can set voltage threshold that correspond to given percentage of remaining capacity

• However, true voltage corresponding to 7% depends on current and temperature

EDV2

EDV1


Learning before fully discharged - with current and temperature compensation

4.2V

EDV2 (I1)

EDV2 (I2)

OCV

• Modeling last part of discharge allows to calculate function V(SOC, I, T)

• Substituting SOC=7% allows to calculate in real time CEDV2 threshold that corresponds to 7% capacity at any current and temperature

CEDV Model: Predict V(SOC) under any current and temperature

CEDV


CEDV Formula

CEDV = CV - I*[EDVR0/4096]*[1 + EDVR1*Cact/16384]* [1 – EDVT0*(10T - 10Tadj)/(256*65536)]*[1+(CC*EDVA0)/(4*65536)] * age

Where: CV = EMF*[1 – EDVC0*(10T)*log(Cact)/(256*65536)] Cact = 256/(2.56*RSOC + EDVC1) – 1 for (2.56*RSOC + EDVC1) > 0 Cact = 255 for (2.56*RSOC + EDVC1) = 0 EDVC1 = 2.56 * Residual Capacity (%) + “Curve Fit” factor Tadj = EDVTC*(296-T) for T< 296oK and Tadj < T Tadj = 0 for T > 296 oK and Tadj max value = T age = 1 + 8 * CycleCount * A0 / 65536.


CEDV Model Visualization

3% 4% 5% 6% 7% 8% 9%

Actual battery voltage curve

Voltage OCV curve defined

by EMF, C0

OCV corrected by I*R (R is defined by

R0, R1, T0) I*R

Further correction by low temperature (TC)

Reserve Cap: C1 shifts fit curve laterally Battery Low


Impedance Track Fuel Gauging

• Combine advantages of voltage correlation and coulomb counting methods

• State of charge (SOC) update: – Read fully relaxed voltage to determine initial SOC and capacity

decay due to self-discharge – Use current integration when under load

• Parameters learning on-the-fly: – Learn impedance during discharge – Learn total capacity Qmax without full charge or discharge – Adapt to spiky loads (delta voltage)

• Usable capacity learning: – Calculate remaining run-time at typical load by simulating voltage

profile do not have to pass 7% knee point


Current Direction Thresholds and Delays

Example of the Algorithm Operation Mode Changes With Varying SBS.Current( )

1

2

3

4

5

6

7

1. CHG relaxation timed 2. Enter RELAX mode 3. Start discharging 4. Enter DSG mode 5. DSG relaxation timed 6. Enter RELAX mode 7. Start charging 8. Enter CHG mode

8


What is Impedance Track? 1. Chemistry table in Data Flash:

OCV = f (dod) dod = g (OCV)

2. Impedance learning during discharge:

R = OCV – V

I

Or, for bq78PL114/116: R = dv/di (Simultaneous Meas)

3. Update Max Chemical Capacity for each cell

Qmax = PassedCharge / (SOC1 – SOC2)

4. Temperature modeling allows for temperature-compensated impedance to be used in calculating remaining capacity and FCC

5. Run periodic simulation to predict Remaining and Full Capacity

10,000 foot View


Close OCV profile for the Same Base-Electrode Chemistry

• OCV profiles close for all tested manufacturers

• Most voltage deviations from average are below 5mV

• Average DOD prediction error based on average voltage/DOD dependence is below 1.5%

• Same OCV database can be used with batteries produced by different manufacturers as long as base chemistry is same

• Generic database allows significant simplification of fuel-gauge implementation at user side

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 13.4

3.67

3.93

4.2

Manufacturer ABCDE

DOD, fraction

Vo

lta

ge,

V


Resistance Update

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

100

200

300

400

dod

Ra

Before Update

Discharge direction


Ra Table: Interpolation and Scaling Operation • R = (OCV – V) / Avg Current. Averaging

method is selectable

• Resistance updates require updating 15 values for each cell

• A new resistance measurement represents the resistance at an exact grid point. Exact value found by interpolation

• All 14 grid points are ratiometrically updated from any valid gridpoint measurement. Changes are weighted according to confidence in accuracy

• Resistance updates in bq78PL114/116 are very much different – see the technical reference manual for details.

Grid

0

Grid

14

k: P

rese

nt g

rid

m:

Last

vis

ited

grid

Ra_new Ra_old

Step 1

Step 2

Step 3

Interpolation

Scale “After”

Scale “Before”


Timing of Qmax Update

QMAX is learned from the change in SOC and Passed Charge between two qualified points in time, P1 and P2.

The learning occurs when two consecutive relaxation periods are separated by a period of current flow.

Qmax = PassedCharge soc1 – soc2


FCC Learning

0 0.2 0.4 0.6 0.87200

7400

7600

7800

8000

8000

9000

1 .104

1.1 .104

1.2 .104

1.3 .104

SMB FCCtrue FCC

Ra gridsVoltage

SMB FCCtrue FCC

Ra gridsVoltage

DOD

FCC

, mA

h

V, m

V


RemCap Simulation (concept)

Constant Load Example

I

Qstart ΔQ ΔQ ΔQ

ΔQ/2

ΔQ/4

. . . . . RsvCap

Vterm

Δ V > 250mV

EDV

V

(loaded)

Start of discharge

RemCap

Time

Time

OCV I*R


Z-track Accuracy in Battery Cycling Test

0 50 100 150 200 250 3001.5

1

0.5

0

0.5

1

error at 10%error at 5%error at 3%

Cycle Number

Rem

aini

ng C

apac

ity E

rror

, %

• Error is shown at 10%, 5% and 3% points of discharge curve

• For all 3 cases, error stays below 1% during entire 250 cycles

• It can be seen that error somewhat decreases from 10 to 3% due to adaptive nature of IT algorithm


Property CEDV Impedance Track

Worst error new, learned +/-2% +/-1%

Worst error aged, learned +30% (+/- 15% with age data) +/-2%

Data collection 3 temperatures, 2 rates,

Fitting to obtain parameters.

2 weeks

Chemistry selection test,

Optimization cycle

1 week

Instruction flash small large

Voltage accuracy requirement 20mV/pack 3mV/pack

State of charge initialization (host side requirement)

No

Yes

FCC temperature compensation

No (with rare exceptions)

Yes

FCC rate compensation No (with rare exceptions) Yes

Learning cycle in production required Not required

CEDV, Impedance Track Comparison


37

bq27620 Impedance Track™ with Dynamic Voltage Correlation (IT-DVC)


bq27620 System Side IT-DVC Fuel Gauge

• Patented Impedance TrackTM battery fuel gauging technology.

• Patent pending Impedance Track – Dynamic Voltage Correlation (IT-DVC) developed to estimate the battery states in real time by using voltage and temperature only without current sensing resistor.

• The current-voltage relationship of battery is modeled to estimate current.

• The estimated current is used in Impedance Track Algorithm to calculate State of Charge.

• Integrated 2.5V LDO regulator • Calculates remaining charge under all

conditions of discharge rate, temperature, and cell age.

38

Host System

Single Cell Li-IonBattery Pack

PACK-

PROTECTIONIC

CHGDSG

BatteryGood

I2CT

PACK+VoltageSense

BatteryLow

FETs

bq27620

Power Management

Controller

LDOREG25 REGIN

VCC

DATABI/TOUT

TS

1.75

2.69

0.62

5 (m

ax)

0.30

0.50

WCSP

• Package options: – WCSP 15-ball, 1.75 x 2.69 mm, 0.625 max thickness, 0.5mm pitch


IT-DVC: Principles and Algorithm


IT-DVC Introduction

• Development target: battery fuel gauge without the current sensing resistor

Current sensor

Idea: replace this with algorithms and firmware


Circuit model

• VOC a function of SOC

• Rint is internal resistance

• Rs and Cs model the short term transient response

• RL and CL model the long term transient response

• Vbatt and Ibatt are the battery voltage and current

• All parameters are function of temperature and battery age

Voc(SOC)

Rint RL

CS CL

RS Vbatt

Ibatt

Transient model


IT-DVC Introduction

• IT-DVC was developed to estimate the battery states in real time by measuring voltage and temperature only

• The current-voltage relationship of batteries was modeled as a piecewise linear system

• Battery current is estimated by using the model. Other battery states (SOC,TimeToEmpty, etc) are derived by using the estimated current instead of measured current inside the Impedance Track Algorithm

• The SOC estimation is still current based (using estimated current)


Impedance Track Algorithm

Work Flow

Battery test data

Identify the parameters of dynamic battery model

Difference equations for current estimation

Offline algorithms; Have been implemented in MATCHCAD

Impedance update

IT-DVC algorithm

TimeToEmpty,FCC etc

Iest,DOD

Real time algorithms, in Firmware


Estimating battery current and DOD estimation result

0.1 1.06008 103× 2.12006 103× 3.18004 103× 4.24002 103× 5.3 103×1−

0.6−

0.2−

0.2

0.6

1

DOD error(%)

1

1−

CIest_2RC CI−( )→

100⋅

Qmax

5300t1 t

1.291 104× 1.327 104× 1.362 104× 1.398 104× 1.433 104× 1.468 104×150−

130−

110−

90−

70−

50−

30−

10−

10

Estimated Current(mA)True Current(mA)

10

150−

f_Iest_2RC

I

1468512911 AG_SET rows I( ) 1−( )


Pulsed load - 1700mAh capacity battery

Estimate Current in Real Time

3.6 103×

3.72 103×

3.84 103×

3.96 103×

4.08 103×

4.2 103×

4.194 103×

3.658 103×

V

4.137 104×0 index2

0 3.6 104× 7.2 104× 1.08 105× 1.44 105× 1.8 105×255−

188−

121−

54−

13

80

Estimated CurrentTrue Current

Iest_best 0⟨ ⟩

I

t


0 3.6 104× 7.2 104× 1.08 105× 1.44 105× 1.8 105×0.01−

6− 10 3−×

2− 10 3−×

2 10 3−×

6 10 3−×

0.01Error of DOD

0.01

0.01−

Iest_best 1⟨ ⟩ CI−

Qmax

→

1800000 t

Error of DOD< 1%

Initial Verification: Sanyo 1700mAh battery

0 3.6 104× 7.2 104× 1.08 105× 1.44 105× 1.8 105×0

0.2

0.4

0.6

0.8

1Estimated DODTrue DOD

1

0

Iest_best 1⟨ ⟩−

Qmax

CI−

Qmax

1800000 t


Verification: Handset load

0 5 103× 1 104× 1.5 104× 2 104× 2.5 104×0.02−

0.014−

8− 10 3−×

2− 10 3−×

4 10 3−×

0.010.01

0.02−

Iest 1⟨ ⟩ CI

Qmax+

250000 t

DOD error<2%

0 5 103× 1 104× 1.5 104× 2 104× 2.5 104×355−

274−

193−

112−

31−

50

Estimeated CurrentTrue Current

50

355−

Iest 0⟨ ⟩

I

250000 t

Sanyo 898 mAh battery


Estimated Current vs True Current – portable electronic device

1 104× 1.5 104× 2 104×400−

300−

200−

100−

0Estimated CurrentTrue Current

20

347−

A ein⟨ ⟩

A in⟨ ⟩

238578787 A tn⟨ ⟩


0 4 103× 8 103× 1.2 104× 1.6 104× 2 104×3.2 103×

3.4 103×

3.6 103×

3.8 103×

4 103×

4.2 103×

0

20

40

60

80

100

RSOCVoltage

Temperature=25C; Load=C/5; Voltage vs RSOC

Logged SOC vs True SOC


0 10 20 30 40 50 60 70 80 90 1002−

0

2

4

SMB RSOC

RSOC

RSO

C e

rror

Temperature=25C; RSOC error

Logged SOC vs True SOC


Summary

• IT-DVC works as a firmware implemented coulomb counter

• Regular Impedance Track™ is applied with these estimated variables

• SOC error <3%

• bq27620 with IT-DVC is scheduled for samples & RTM in Q4 2011

• Systems that benefit most from this device: – Need small board size (no sense resistor space) – Need low cost (no sense resistor cost) – Need high current support, can not afford losing power for sense resistor – Requires high accuracy provided by Impedance Track framework – Need short development time provided by chemical ID database support for

most new cells without characterization time

51


Q & A

Gauging Algorithm Comparisons · 2016. 10. 10. · m := cell mass . c. p:= specific heat . h. c :=...

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