EVS28 KINTEX, Korea, May 3-6, 2015

A study on the State of Charge

estimation based on internal

resistance and power counting for

Lithium ion battery

Hoyoung Park1, Changyoul Choi1, Changoug Hong1

Hyeoundong Lee2 1Electric Energy Engineering Team, Hyundai Mobis , 17-2, 240 Beon-Gil, Mabuk-Ro,

Giheung-Gu, Yongin-Si, Gyeonggi-Do, Korea, phy5119@mobis.co.kr 2Eco Engineering Group, Hyundai Mobis

Contents

I. Introduction

II. Mathematical Approach

III. Scenarios and evaluation test

IV. Results

V. Conclusion

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Introduction

I. State of Charge (SOC) estimating methods for Battery

3

State of Charge

(SOC)

Proposed method: Power counting

Ah Counting

AC-impedance

Extended Kalman Filter

Text

Open Circuit Voltage Open Circuit Voltage (OCV)

- High Accuracy for No load

- Difference between real voltage

and operating voltage

- The simple and stable method if

the current is measured accurately.

- Cumulative error caused by current

sensor and initial SOC.

- High Accuracy

- A lot of data

- Difficulty for real-world application

- High Accuracy

- Non-linear parameter affected by

external factors

Simplicity for implementation Difficulty for implementation

Introduction

II. SOC Algorithm development process

4

Text

SOC Algorithm

Modeling (MATLAB SIMULINK)

Temperature &

Driving Simulation

Pattern Test

(Battery system)

Vehicle Verification

- Real world test

Mathematical Approach

I. Theoretical approach

Battery hysteresis characteristics

- Estimation of OCV during cycling

- Calculation of the capacity loss caused by Internal Resistance(IR)

Correlation between battery capacity(Ah) and energy(Wh)

- State of Charge (SOC) ≒ Battery remaining capacity (Ah)

- Energy (Wh) = Battery capacity (Ah) x Battery terminal voltage (V)

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Battery hysteresis of a manganese-based

Li-ion battery Basic concept for power counting Battery energy

IR

Mathematical Approach

II. Mathematical approach

Mathematical modeling application through simple equivalent circuit

- Instead of using current, power can be used for SOC estimation.

- Time related power is converted into energy.

- The IR calculation for energy loss and comparison with a temperature related parameter value.

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Charge Discharge

Wbattery = Wload + Wloss Wbattery = Wload - Wloss

Mathematical Approach

Utilization of SOC and internal resistance for estimating

- Parameter: IR

- OCV Estimation / Noise Removal / Relaxation Effect using IR

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SOC-OCV table

Power counting

Internal resistance

Input

• Current

• Voltage

• Temperature

• SOC

Output

• SOC

OCV estimation Relaxation effect

Scenarios and evaluation test

I. Scenario

Analysis tool & driving simulation pattern

- MATLAB SIMULINK & UDDS/HWFET

Simulation scenario

- Patterns compatible with HEV battery system

- Input value: Uniformly “urban” and “highway”

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Converted into

the current pattern Urban dynamometer Driving Schedule (UDDS)

Highway Fuel Economy Test (HWFET)

-120

-60

0

60

120

0 500 1000 1500

Cu

rren

t (A

)

-100

-50

0

50

100

0 200 400 600 800 1000

Cu

rren

t (A

)

Time (sec)

UDDS

HWFET

Scenarios and evaluation test

II. Evaluation test

Simulation & Battery system test by temperature change

- Simulation test by temperature and driving simulation mode

- Parameter revision with simulation test results

Vehicle test

- Verification and operation of car according to driver’s actual situation

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Content

Temperature Simulation: -20℃, -10℃, 0℃, 5℃, 10℃, 25℃, 45℃

Battery system unit: -20℃, -10℃, 25℃

Initial SOC 50%, 90%

Pattern type UDDS, HWFET

Battery system unit test condition

Vehicle test condition

Content

Vehicle HG HEV (Hyundai)

Region Stop-state, urban, urban and highway combination

Electronics work AVN, Air-conditioner, heater and so on

I. Simulation test results

Input of battery system test result for proposed model

- Establishment of reference SOC with the measured OCV after rest for hours

- Comparison of SOC after end of driving and sufficient rest time

SOC estimation error within 2% from -20 to 45℃.

0

20

40

60

80

100

0 1000 2000 3000

SO

C (

%)

시간(sec)

Ambient temp: -20℃

Results

10

0

20

40

60

80

100

0 1000 2000 3000 4000

0℃

0

20

40

60

80

100

0 1000 2000 3000 4000

25℃

UDDS

HWFET

SOC Error(%)

-20℃ -10℃ 0℃ 5℃ 10℃ 25℃ 45℃

UDDS 0.17 0.54 0.23 0.02 0.01 0.07 0.15

HWFET 0.24 1.08 0.43 0.54 0.17 1.13 1.75

Results

II. Battery system unit test results

Evaluation of manganese-based Li-ion battery system

- Reproducibility confirmation of simulation results when applied to actual application

- Comparison of SOC after end of driving and sufficient rest time

SOC estimation error within 1% at -20, -10, and 25℃.

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0

20

40

60

80

100

0 1000 2000 3000

SO

C (

%)

Time (sec)

0

20

40

60

80

100

0 1000 2000 3000

0

20

40

60

80

100

0 1000 2000 3000

Ambient temp. : -20℃

Red : Simulation

Blue : Experimental

-10℃ 25℃

SOC Error(%)

-20℃ -10℃ 25℃

UDDS 0.59 0.91 0.03

HWFET 0.71 0.61 0.13

UDDS

HWFET

Results

III. Vehicle test results

Test driving condition : Stationary-state, urban and highway

- The reference SOC is derived from the OCV measured after vehicle rests for hours.

- Comparison of SOC after end of driving and sufficient rest time

SOC estimation error within 2% at stop-state, urban, urban and highway

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SOC Error(%)

Test 1 Test 2 Test 3

Stop-state 0.01 0.56 -

Urban 1.01 1.07 0.73

Urban and highway 1.18 0.78 0.34

20

40

60

80

100

0 1000 2000 3000 4000 5000

SO

C (

%)

Time (sec)

20

40

60

80

100

0 1000 2000 3000 4000 5000

Red : Simulation

Blue : Experimental

Stop-state Urban and highway

20

40

60

80

100

0 1000 2000 3000 4000 5000

Urban

Conclusion

I. Conclusion

Power and simple resistance circuit were introduced resulting in reducing

current sensor noise and reflecting relaxation effect of the battery for

generating novel SOC estimation method

A feasibility of the proposed algorithm approach is verified by battery system

and vehicle test

SOC error is within 2% in the vehicle

II. Future work

To improve SOC accuracy, additional work will be carried out as follows.

- Additional SOC verification in the vehicle at low temperature

- Continuous data acquisition under the real driving condition

- Parameter optimization based on accumulated data

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