Akku4Future - Methods for Data Generation to ...

2015 International Conference on Electrical Drives and Power Electronics (EDPE)

The High Tatras, 21-23 Sept. 2015

Akku4Future - Methods for Data Generation to Compute State Indication Alexander Elbe, Stephan Thaler Department of Engineering and IT Carinthia University of applied Sciences Europastraße 4, A-9500 Villach, Austria [email protected], [email protected] Abstract— To estimate state-of-charge (SOC) and state-ofhealth (SOH) of a lithium ion battery as exact as possible, the terminal voltage, the terminal current and the cell temperature have to be measured. The easiest model of a battery is an ohmiccapacitive (R-C) series-parallel circuit. Two measurement methods are shown how to get ready to estimate SOC and SOH in a simple but precise manner. The first measurement is a defined capacity package being discharged a battery until the battery is empty. The packages give data points in the correlation curve for open-circuit-voltage (OCV) and SOC. This is base for SOC estimation. To take degradation into account hundred full cycles are examined on the test cells with 15 parameter variations on the above mentioned three parameters voltage, current and temperature. Keywords—battery management; charge; battery degradation

state-of-health;

state-of-

I. INTRODUCTION In every battery powered system the battery itself is a major part. If it is faulty, the whole system is not working; for instance if a mobile phone has a battery discharge time of five hours under normal usage instead of five days the phone will most likely be replaced. Monitoring the battery status identifies failures which can be communicated to the user. The battery can be replaced instead of the whole phone. One step ahead, the monitoring data will enable the phone´s battery management system to evaluate the user behaviour and alerts the user before the battery status is critical. The state of a battery describes its functionality. Therefore the term state of function (SOF) is used in literature. [1] The SOF tells weather the battery does what it was designed for or not. After several years in use and after many hundred charge and discharge cycles, the battery will degrade. The aim of this project is to show how relevant parameters are measured to estimate the state of a battery cell. Relevant parameters are the terminal voltage, the charge- and discharge current, the temperature of the cell and the time in use. The precise knowledge of the current state (SOC, SOH) of a mobile devices’ energy storage (mainly electrochemical energy storage: batteries) is the key factor for the active life extension of such storage systems. With the large and expensive traction batteries a need of potentially lifeprolonging battery system can be seen in the context of electric mobility. To display this information, the battery cells must be characterized. The characterization consists of the

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measurement procedures and charging methods described in chapter III. One problem is the accuracy of the residual-range estimation of electric vehicles in cold temperatures. Since the residual range indicator is based on SOC, a reliable determination of the SOC even at temperatures below the freezing point is needed. Therefor the test cells being charged and discharged at four significantly different temperature points. The voltage responses of the test cells during charging are seen in Figure 2 and the voltage responses during discharging are displayed in Figure 3. The plots of voltage responses over time show how big the influence of temperature is on the cell during use. What such capacity losses look like at temperatures around the freezing point can be seen in Figure 1. The comparison of the charge and discharge at temperatures of -10° C, 4° C, 25° C, and 45° C shows that there are differences in the charged or discharged capacity. Thus, the usable storage capacity at -10°C is reduced by 28 %, compared to the capacity at 25° C. [2] In order to inform the user of a battery powered system about the actual charge level of the battery at each temperature precisely, the cells used are previously measured within their operation window. The safety operation window of the test cell is obtained by the manufacturer’s data sheet in [3]. This allows a suitable prediction of the state of health and state of charge of the test cell in further steps of development.

Fig. 1. Temperature-dependency of charge and discharge capacity of Samsung ICR1650-22P [2]

2015 International Conference on Electrical Drives and Power Electronics (EDPE)

The High Tatras, 21-23 Sept. 2015

Fig. 4. Samsung ICR18650-22P cell

Fig. 2. Comparison of V-responses at test temperatures during charging with charge current of 0,2 C [2]

The cells typical capacity Qnom, its minimum capacity Qnom_min and the rated capacity (at 1 C discharge) [1] are given under defined cut-off criteria (IDCH = 0.2 C; Ucut-off = 2.75 V) [3]. The voltage operation window of 2.75 V to 4.25 V is defined. The charging and discharging current maxima are set to 2150mA (=1 C) for charging and 10 A (=4.65 C) for discharging. Temperature operating window between -10° C to 50° C for charging and -20° C to 70° C for discharging are set for safe cell operation. Life expectance is given at 500 cycles (1 C charge / 1 C discharge under Ucut-off) until the wear out level of 90 % of Qnom is reached. [3] III. METHODES FOR CHARACTERIZING THE TEST CELL

Fig. 3. Comparison of V-responses at test temperatures during discharging with discharge current of 0,2 C [2]

The impact of temperatures below 25° C is significantly visible in Figure 2 and Figure 3 via the indication of the used capacity whereas the capacity increases slightly at higher temperatures (45° C). II. SELECTION OF THE TEST CELLS During the design process of any battery powered system, the main question is how much power and energy can be delivered? Li-ion batteries are diversified into power cells and energy cells. A power cell delivers high energy over a short time period while an energy cell provides lower currents over a longer time period. NMC [4] gives a well-balanced li-ion battery chemistry. Thus it is predestined for use in electric vehicles. The test cell should be of the latest battery generation, suitable for the possible application in electric powertrain (production quality issues). The selected cell is the Samsung SDI ICR 18650-22P cell and is shown in Figure 4.

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A. Correlation curve for open-circuit-voltage and state-ofcharge at 25° C The maximum charge and discharge capacity is measured by the Coulomb - Counting method. [5] The rated capacity is 2150 mAh. The cell is first loaded with the CC - CV charging method [4] until the final charge - criterion (Icharge-end = 0.02 C; Vterminal = 4.2 V) is reached. After 8 hours of waiting time (for the relaxation of the terminal voltage), a charging process is started with 0.05 C until the final charge - criterion is achieved. This wait - charging - sequence is performed three times in total. The reached capacity of 2184 mAh at 25° C is defined as maximum charge capacity. After finishing charging the test cell is discharged with 0.2 C in CC mode to the discharge – cut - off - criterion (Vterminal = 2.75 V) is reached. After 8 hours of waiting, the cell is discharged at 0.05 C until cut-off voltage. The reached maximum discharge capacity equals 2209 mAh. This sequence is carried out three times in total. For the determination of the relation between terminal voltage and SOC the test cell is fully discharged with 29 constant current pulses. [2] The capacity of each discharge pulse and the terminal voltage after the waiting time are measured after each pulse. The correlation curve is formed by 29 data points from the terminal voltage and discharged capacity. The correlation curve for the test cell under given conditions is represented by Figure 6. To shorten the time series of measurements to a minimum, the waiting periods between pulses are varied in dependence of the SOC. The waiting time between the current pulses is increased gradually from eight hours at SOC = 1 to 24 hours at SOC = 0. This relationship is explained by the difference between the terminal voltage after eight hours or 24 hours with the terminal voltage right after the discharge pulse and is shown graphically in Figure 5. The voltage difference is color dark blue (0 mV

2015 International Conference on Electrical Drives and Power Electronics (EDPE)

The High Tatras, 21-23 Sept. 2015

difference) applied to dark red (10 mV) in the background. It will be seen that in SOC < 5 % only waiting times of more than 22 hours sufficiently accurate provide results for the terminal voltage. The waiting time (y-axis) over the SOC (x-axis) shows a comparison between linear and quadratic increase of waiting time. Based on this measurement, a terminal voltage to SOC comparison table is created.

Fig. 7. Current, voltage response and DC resistance of the test cell [7]

Fig. 5. Correlation between waiting time and SOC after a current discharge pulse in dependence of the difference of terminal voltage right after puls and x-hours after pulse [2]

Fig. 6. measured correlation curve between the OCV and the SOC of the test cell

B. Dynamic behavior described via the current pulses The model parameters have to be derived from the above described measurements to describe the test cell via the Randles circuits [6] and more sophisticated equivalent circuits. The terminal voltage is monitored during the current pulse and after the pulse is finished by imprinting a constant current. This is known as the voltage-step response. This experiment is done for 15 variations in the current and temperature level. [2] The charge and discharge current has to be the same. The reconstitution of the terminal voltage gives the knowledge of how the two main over-potentials (charge transfer, diffusion) inside the cell behave. The current pulses either discharge the cell to a SOC of zero percent or charge the cell completely. Figure 7 shows the current pulses in the second row. The internal resistance of the cell is derived by the voltage-step response.

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C. Planned cell degradation by experimental design The values for the charge rate, discharge rate and ambient temperature are varied within the spectrum given by the data sheet to secure an accelerated aging of the cell. The statistical model behind the used method calculates the lowest number of the experiments needed to achieve the highest amount of expressiveness. This ensures a meaningful description of the cell throughout the operation area. The cell temperature, the charge current and the discharge current is varied by using the "Design of Experiments" method [8] in the way that the cells’ whole operation spectrum can be generated with as few experiments as possible. With 15 experiments from the Central Composite Experimental Design [8] the parameter variation is generated like it’s shown in Table 1. The boundaries of the three parameters are out of the manufacturer's data sheet where the temperature range is set between 0° C - 60° C, the range of values for the discharge current is set from 538 mA -10000 mA and the discharge current is set between 538 mA – 2150 mA. The degradation measurements consist of 100 charge and discharge cycles per experiment. This leads to very long measurement times. That for a measurement system was built up with six channels to be able to perform parallel experiments. After 100 cycles, the experiment is done and the capacity loss is determined by comparing to initial one at 25° C. For reference, the defined maximum value of the capacity equals 2209 mAh. Table 1 shows the absolute values of the discharge capacity and the relative values of the loss of capacity are listed in percent. As expressed in Table 1 as a percentage of capacity reduction, the decrease in capacity in Figure 7 can be seen. The terminal voltage profile over the discharge time shows the discharge behavior of experiments 3, 7 and 15. The discharge capacity is at 60° C for 3 cycles is the biggest capacity. After 100 cycles at 60° C, the capacity decreased by 4.8%, compared with the defined nominal capacity. Of approximately the same capacity loss (4.95 % after 100 cycles) was determined at 25° C in Experiment 7. [2] Thus, the charging current of 1 C at 25° C is almost equally stressful for the cell as 0.62 C charging current at 60° C whereas the capacity after 3 cycles in experiment 7 is 2.46 % lower than in experiment 15 after 100 cycles. Experiment 3 at 4° C shows the reduced discharge

2015 International Conference on Electrical Drives and Power Electronics (EDPE) capacity of 1937 mAh after 3 cycles and 1734 mAh after 100 cycles.

Fig. 8. terminal voltage courses of discharging over time for experiments 3, 7 and 15 after 3 cycles and 100 cycles

The High Tatras, 21-23 Sept. 2015

constant-current pulses, and to discharge the batteries in the constant current (CC) or constant-current pulse mode. Furthermore six Pt100 temperature converter cards are installed to monitor temperature of each of the six test cells. Three temperature conditioning devices keep the ambient temperature for the test cells constant. A schematic architecture of the measurement equipment is shown in Figure 9.

Fig. 9. Schematic architecture of the used measurement equipment

TABLE 1: DATASET OF EXPERIMENTS WITH DISCHARGE CURRENT, CHARGE CURRENT, TEMPERATURE, ABSOLUTE CAPACITY AFTER 100 CYCLES AND CAPACITY RETENTION AFTER 100 CYCLES

The software structure to control the whole setup is built up in three independent loops: one to deal with the data acquisition, down sampling and data storage; one to observe the control structure; and one to visualize the main data and the Capa_ret_ 100 [%] controls for the operator. Figure 10 shows schematically the relationships between the three main loops, data exchange and program flow. 1,68

I_DCH [mA]

Temp [K]

I_CH [mA]

Capa_100 [mAh]

Exp 8

538

298

1344

2113,9

Exp 12

2456

319

864

2095,5

2,53

Exp 14

2456

319

1823

2091,2

2,73

Exp 9

5269

298

1344

2061

4,14

Exp 15

5269

333

1344

2046,9

4,80

Exp 7

5269

298

2150

2043,6

4,95

Exp 6

5269

298

538

2038,63

5,18

Exp 13

8082

319

1823

2034,9

5,35

Exp 11

8082

319

864

2033,7

5,41

Exp 10

10000

298

1344

1921

10,65

Exp 5

2456

277

864

1875

12,79

Exp 3

8082

277

864

1734,3

19,33

Exp 2

2456

277

1823

1438,5

33,09

Exp 4

8082

277

1823

218,89

89,82

Exp 1

5269

263

1344

0,39

99,98

IV. THE TEST AND MEASUREMENT ENVIRONMENT The established test setup comprises one Windows PC running the NI LabView, two NI DAQ Cards, six active loads (AL) and six power supplies (PS). The PC controls the PS and AL continuously via USB to charge the test batteries with a standard constant current, constant voltage (CC-CV) or

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Fig. 10. Software structure for controlling, data acquisition and GUI

2015 International Conference on Electrical Drives and Power Electronics (EDPE) V. RESULTS Degradation phenomena occur at low temperatures more apparent than in mid - sized to high temperatures. The relative loss of capacity at 4° C after 3 cycles is 10 % and at a number of 100 cycles, the available discharge capacity reduced by 20 %. The maximum achievable SOC of 80 % means in the automotive industry already that the SOH = 0 what is already resulting in a battery exchange. The battery pack in modern electric vehicles is supplied at temperatures around the freezing point of the charging station with energy for heating. The discharge capacity at high temperatures increases after 3 cycles to 1.5 % higher than the rated capacity and there is reason to believe that the power output at temperatures of 60° C is the highest. The duration of the measurements for creating the correlation curve between SOC and terminal voltage was able to be reduced through the use of squared waiting time decrease by 30 %. Application of Experimental Design Method for parameter variation of experiments in combination with the 6 - channel measurement system also gives a time advantage (15 vs. 27 experiments) compared to standard full - factor variation of parameters in a single measurement setup. VI. CONCLUSION To estimate the state of a lithium ion battery far more sophisticated methods to characterize, to measure and to calculate test cell parameters and the corresponding variables in battery models will be needed than any known chemistries like the lead-acid battery or the nickel-metal-hydride battery. The aims of this project are to identify battery stressors and to model the battery behaviour in any state. Further to characterize one test cell by measuring the terminal voltage, the load current, the surface temperature as well as the number of charge / discharge cycles and to merge these information to one system which is able to estimate the state of charge and the state of health of a cell with unknown history (cycle number, age). We could prepare the characterization of the test cell by literature research. The characterization process has to be designed overlooking the battery influencing factors like charge voltage higher than 4.2 V, charge currents higher than 2 C, discharge voltage lower than 2.75 V and discharge current higher than 4.65 C. Charge and discharge temperature outside of 0° C and 60° C have a bad influence on the lifetime of the investigated battery. We have figured out different steps to characterize a li-ion battery. The first step is to define the 100% state of charge. The next step is to correlate between the open circuit voltage and the state of charge. Further to define the dynamic behaviour of the cell in terms of finding variables and parameters describing the battery model and the degradation of the cell by controlled cycling under specified conditions by the use of experimental design.

EDPE 2015

The High Tatras, 21-23 Sept. 2015 REFERENCES

[1] [2] [3] [4] [5]

[6] [7]

[8] [9]

W. W. Andreas Jossen, Moderne Akkumulatoren richtig einsetzen, München: Reichardt Verlag, 2006. A. Elbe, Akku4Future-Measurement methods for lithium-ion battery systems, Villach: AV Akademikerverlag, 2014. S. SDI, "ICR18650-22P specification sheet," Samsung SDI Co. Ltd, Battery Business Division, 2010. T. B. R. David Linden, Linden´s Handbook of Batteries, McGraw-Hill, 2011. C.-S. M. Y.-P. C. Y.-C. H. Kong Soon Ng, Enhanced coulomb counting method for estimating state-of-charge, Applied Energy 86, pp. 1506-1511, 8 Jänner 2009. J. E. B. Randles, Kinetic of rapide electrode reactions, in Discussions of the Faraday Society, 1947. F. Niedermayr, Akku4Future-Report for Workpackage 3/4, 2014. [Online]. Available: http://www.akku4future.eu/wpcontent/uploads/2014/04/Abschlussbericht_WP3_4_FINAL.pdf. [Accessed 2015]. C. GmbH, Experimental Design, User's Manual, Petershausen, 2012. A. Böhm, Aufbau und Parametrierung von Batteriemodellen basierend auf elektrischen Ersatzschaltkreisen, TU München, 2011.

Alexander Elbe, MSc studied Electrical Energy and Mobility Systems at the CUAS and graduated with MSc in 2013. His thesis has been written within the research project Akku4Future, where he developed measurement strategies to take electrochemical processes inside a lithium ion cell into account to identify the state of charge and the state of health. Since 2012 he is working as junior researcher at the CUAS, where he is the contact person for EV traction battery topics. Stephan Thaler, MSc was born in Villach, Austria, in 1978. He received the BSc. degree in systems engineering in 2010 and the MSc. degree in Biomimetics in 2012 from the Carinthia University of applied science. Since 2013, he is working as junior researcher at the Engineering & IT department at the CUAS. His research interests are in the area of "renewable energies" in terms of the development of new energy and storage systems and the optimization of existing technologies.