Lithium-ion battery performance improvement based on capacity recovery exploitation

Electrochimica Acta 114 (2013) 750–757

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Lithium-ion battery performance improvement based on capacity recovery exploitation Akram Eddahech ∗ , Olivier Briat, Jean-Michel Vinassa University Bordeaux, IMS, UMR 5218 CNRS, F-33400 Talence, France

a r t i c l e

i n f o

Article history: Received 24 July 2013 Received in revised form 24 September 2013 Accepted 16 October 2013 Available online 28 October 2013 Keywords: Lithium-ion battery Power cycling Capacity recovery Calendar aging

a b s t r a c t In this work, the performance recovery phenomenon when aging high-power lithium-ion batteries used in HEV application is highlighted. This phenomenon consists in the increase on the battery capacity when power-cycling is stopped. The dependency of this phenomenon on the stop-SOC value is demonstrated. Keeping battery at a fully discharged state preserves a large amount of charge from the SEI-electrolyte interaction when they are in the positive electrode during rest time. Results from power cycling and combined aging, calendar/power-cycling, of a 12 A h-commercialized lithium-ion battery, at two temperatures (45 ◦ C and 55 ◦ C), are presented and obtained results are discussed. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Due to their high performances, namely high energy and power densities, their longer cycle lifetime, Lithium ion (Li-ion) batteries remain the best solution for effectively storing electric energy [1]. Recently, they are the favorable choice and the key enabling technology for energy storage system (ESS) in advanced transportation applications. One example of these applications is the electric vehicle (EV) and the hybrid electric vehicle (HEV), where the battery operates as a main energy source or as part of hybrid energy systems combined either with an internal combustion engine or with a fuel cell [2]. Like requirement is still in increase, on one hand, chemical and electrochemical experts tried to improve lithium battery performances such as their energy/power density, their lifetime and thermal stability [3–5]. On the other hand, electrical and electronic engineering experts tried to accurately model and predict their behavior and performances during applications [6–8]. Basically, the ability of the battery to deliver certain energy and power range is strongly related to its available performances, such as its internal resistance and capacity. However, the battery’s

∗ Corresponding author. Tel.: +33 5 40 00 26 13; fax: +33 5 56 37 15 45. E-mail addresses: [email protected], [email protected] (A. Eddahech). 0013-4686/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.electacta.2013.10.101

performances gradually decrease over lifetime as a consequence of power and capacity losses. Therefore, for an appropriate power and range prediction in HEV and EV, the degradation of battery performances during lifetime has to be considered. Particularly high temperatures, high currents magnitude and high energy throughput are the main factors that strength the deterioration of batteries’ electric characteristics in power cycling [9]. Actually, batteries performances degrade even at rest that means when vehicle is in the parking, well known as calendar aging [10], mainly for a high state-of-charge (SOC) and high temperature. Few works combined calendar and power cycling aging experiments and often lithium batteries aging investigations presented in literature considered separately these modes. Our contribution consists in presenting experiments on combined (power cycling-calendar) aging mode on a high-power lithium-ion battery. This aging mode revealed a phenomenon strongly related to this combination between the continuous power cycling and the rest-time (pause), namely the capacity recovery phenomenon. So in first part, aging experimental setup is presented, then in a next section, the performance recovery phenomenon has been highlighted and the influence of SOC-stop on this phenomenon is presented. Finally, a discussion on temperature impact on powercycling aging is given. A cobalt manganese nickel oxide lithium-ion polymer battery, with a nominal voltage of 4.2 V and the nominal capacity of 12 A h is used to implement the experiments.

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Test temperature is the same and batteries are placed in the same climatic chamber for both of these scenarios. So overall, four batteries are under tests corresponding to two temperatures and two scenarios. For power cycling test, moreover than pause period, the current profile consists of many charge/discharge pulses which present several current levels (from 1.5 C to 3 C for the charge and 1.5 C to 7.5 C in discharge). This profile is fixed by the standard IEC 626601 for HEV-batteries performance testing with just an adaptation in order to respect the battery charge/discharge current limits and power-bench range. Actually, this profile is designed to simulate the main functionalities of such vehicle (HEV) like start/stop, boost and regenerative braking. Test begins with a full-charged cell based on constant current–constant voltage (CC–CV) protocol. In fact, the battery is charged with a direct current of 12A (1 C) during the constantcurrent part and the cutoff voltage was set at 4.2 V. Subsequently, the voltage was held constant at 4.2 V till the current fell to 0.6A (C/20). Then, battery is discharged until 80% of SOC at 1 C; this represents the starting point for the daily power cycling. A series of macro-discharging cycles followed by a macro-charging cycles is applied to the battery. The macro-discharging cycle is composed itself of a series of micro-discharging cycle (profile rich on discharge) repeated until SOC reaches 30% and the macro-charging cycle is composed of a series of micro-charging (profile rich on charge) cycle repeated until the SOC reaches 80%. Macro-cycle is repeated eight time, this last about 20 h, and then the battery is fully recharged (full discharge at 1 C and full CC–CV charge) and discharged to SOC80% to begin the next day. Figs. 1 and 2 show the current profile and voltage response during the micro-discharging and the micro-charging cycles, respectively. Fig. 3 shows the power-cycling chronogram for one day. For calendar test, after five cycling days, the battery is kept at the same test temperature and at a defined SOC abbreviated here

Voltage (V)

0

3.8

3.6

-50

0

50

100 100

150 150 Time (s)

20 00

250

-100 300 30 0

Fig. 1. Micro-discharging current profile and voltage response.

3.8

100

3.6

0

3.4

0

50

100 100

150 150 Time (s)

20 00 00

250 250

Micro-charging current (A)

• In the first, the vehicle is always in use: therefore, continuous power-cycling aging tests are conducted on the battery 7/7 days and 24 h a day. • In the second, the vehicle is used all the week (5 days) but it spends the week-end (2 days) in the parking: therefore, in this case, there is a combination between power-cycling for five days and calendar aging for two days.

50

4

Voltage (V)

The Digatron Battery Testing System is used for implementing aging profiles and data acquisition. It is a power processing system which has the flexibility to implement any electrical driving cycle, and can offer a voltage range of 70 VDC and a maximum current of ± 1000 A. During the experiment the current, the voltage and the temperature data is collected by the processing software (BTS-600). The batteries were placed in an isothermal chamber during tests to ensure temperature control. Experiments are carried out at both 45 ◦ C and 55 ◦ C. From literature and previous studies on lithium-ion battery aging, it was demonstrated that, for power cycling aging, temperature (T), the state-of-charge variation (SOC) and the magnitude of the current pulse (I) were considered as the main factors of aging [11,12]. However, for calendar aging, SOC and temperature are the main influencing parameters on battery performances [13]. As aging tests using real operation conditions are very time and cost intensive, accelerated aging tests are performed using high temperatures and high SOC variations amplitude, namely 50%, (SOC = SOCmax − SOCmin ). Two scenarios for the experiments are envisaged:

4.2

Micro-discharging current (A)

2. Combined power-cycling/calendar aging experiments

751

-100 300 30 0

Fig. 2. Micro-discharging current profile and voltage response. Micro-charging current profile and voltage response.

“Stop-SOC” for 48 h. The influence of this parameter is discussed in the next section. Aging experiments are performed during a long time and tests will be stopped only when fixed criteria of end-of-life are reached, namely 20% of loss of capacity and/or 100% of internal resistance rise. Fig. 4 shows the batteries under test. 3. Lithium-ion battery performances recovery 3.1. Performances recovery phenomenon highlight Batteries performances, namely capacity and internal resistance, are checked from real tests. The battery capacity is calculated before the beginning of the power-cycling and from the full recharge at 1 C rate (12 A) which happens once the 8 times of macro-cycles finishes and at test temperature. Figs. 5 and 6 show a comparison between the batteries capacity issued from both of the scenarios (continuous power-cycling and combined power-cycling/calendar aging mode), respectively, for experiments conducted at 45 ◦ C and 55 ◦ C. These aging results highlighted the capacity recovery phenomenon which consists in the rise of the battery capacity when power-cycling is stopped. From an electrochemical point of view, batteries performances degradation is a result of several simultaneous physic-chemical

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Fig. 5. Recovery phenomenon highlight at 45 ◦ C.

Fig. 3. Power cycling scheme.

processes that occur within the electrode, electrode–electrolyte interface, and within the electrolyte [14], even in current collector according to recent research [15]. Overall, the aging of lithium batteries still be a very complex phenomenon. Various electrochemical processes, occurring within the cell, including intercalation of lithium ions into layered graphite anode and cathode materials, as well as mass transport of lithium ion through the electrolyte, are responsible of materials properties degradation and of the overall batteries performances drop [16,17]. Moreover, from literature, it is widely known that electrolyte decomposition and the corresponding formation of solid electrolyte interface (SEI), is the main aging process in most graphite-based lithium-ion batteries leading to an irreversible capacity loss (due to loss of active lithium) and impedance rise (due to increase in film layer thickness) [18]. This depends primarily on

Fig. 4. Lithium batteries under aging test in the climatic chamber.

the electrolyte formulation and on the specific surface area of the carbon electrode [19]. Basically, the recovery phenomenon is probably related to charge redistribution when there is no charge or discharge force acting on them [20]. That’s why this phenomenon is less visible on internal resistance plots. Fig. 7 shows the internal resistance from the combined aging mode at 45 ◦ C. Fig. 8 represents the internal resistance from both the continuous power-cycling aging test and the combined mode at 55 ◦ C. Actually, this figure report the normalized value which consists in the actual value of battery resistance divided by its value at a battery fresh state. From both of the aging mode, note that the power aging is accelerated with temperature and hence rest time at the week-end slowdowns the battery impedance rise. We represented here, the 10 s internal-resistance calculated using the IEC standard with next equation, Ri =

U I

(1)

with U the voltage rise after 10 s of rest period divided by the discharge current (I), a high pulse power (45 A) of 3.75 C, that takes not only Ohmic resistance into consideration but also diffusion and charge transfer phenomena (see Fig. 9).

Fig. 6. Recovery phenomenon highlight at 55 ◦ C.

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753

3.6

Internal resistance (mΩ)

3.4 3.2 3 2.8 2.6 combined aging mode

2.4 2.2 2

0

50

100 150 Aging days

200

250 Fig. 10. Capacity degradation model for batteries aged at 45 ◦ C.

Fig. 7. Internal resistance from aging with combined mode at 45 ◦ C.

we estimate that the calendar effect is high in this stage of aging. After that, we observe that the pause periods (battery at rest) slow down the aging hence the battery under scenario1, continuous power-cycling, fades more rapidly than the other one. However, at 55 ◦ C, the difference is clearer from the beginning and aging is much accelerated with the continuous power-cycling mode. For the test conducted at 45 ◦ C, a linear equation (−0.0074x + 13.499) could represent accurately capacity fade of the continuous power-cycling test with a determination coefficient R2 = 0.9996. This linear behavior is confirmed with the internal resistance increase which evolves following y = 0.0061x + 2.087 with an R2 coefficient equal to 0.9973. However, for the global degradation range in the combined aging mode, the capacity’s evolution equation must change its slope once aging goes over 90 days it becomes (−0.0058x + 12.876) (see Fig. 10).

2

Normalized resistance (PU)

1.9

Combined aging Power cycling

1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1

0

10

20

30

40 50 Aging days

60

70

80

90

3.2. Influence of the Stop-SOC on the recovery phenomenon

Fig. 8. Comparison between resistance from power cycling and combined aging modes at 55 ◦ C.

When we focus on battery capacity behavior, we notice that at 45 ◦ C, for the first 90 days there no effect of the rest time. In fact, the impact of power-cycling is not visible from the beginning unlike the test at 55 ◦ C because performance of battery tested under combined mode and battery tested under varies similarly;

4

According to scenario 2, before moving to rest period, the battery is discharged until a given SOC at which the battery is kept during the two days; it is called here the “Stop-SOC”. Table 1 shows a quantification of aging and capacity rise due to performance recovery at several battery ages and several Stop-SOC for the battery aged under the scenario 2 at 45 ◦ C. Capacity loss and internal resistance rise are calculated below, Closs =

(Cini − Cactu ) Cini

(2)

Rrise =

(Ractu − Rini ) Rini

(3)

50

with Cini and Rini the initial values and Cactu and Ractu the actual value of the capacity and the internal resistance. For the capacity rise due to recovery phenomenon it is calculated next, 0

3.8

-50 45

50

55 Time (s)

60

65

Fig. 9. Battery voltage response to 45 A pulse from the first micro-discharging cycle at around 80% SOC and 55 ◦ C.

Crise =

(Cafter − Cbefore ) Cini

(4)

with Cafter , the capacity value after the week-end and Cbefore its value before the week-end. Table 2 represents the same quantification for the battery aged according to scenario 2 at 55 ◦ C. Results showed that performances recovery phenomenon is not affected by aging. Actually, the phenomenon is reproduced in the same way even when the battery becomes aged. However, it is mainly dependent on the “Stop-SOC”. Indeed, a higher recovery (around 0.9 to 1%) is obtained when the battery was set at a fully discharged state in the week-end compared to around 0.35 to 0.5 for SOC 10% and SOC 20%.

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Table 1 Quantification of the performance recovery phenomenon at different STOP-SOC and battery age at 45 ◦ C. Aging days

42

Stop-SOC Capacity rise (%) due to recovery Capacity loss (%) Internal resistance rise (%)

20% 0.401 1.92 15.69

111 20% 0.492 5.24 34.62

144

182

232

247

261

305

Fully discharged 1.02 6.58 44.89

20% 0.37 7.54 60.65

Fully discharged 0. 92 9.84 73.98

10% 0.485 10.63 78.08

Fully discharged 1.07 11.15 80.74

Fully discharged 1.05 12.48 85.59

Table 2 Quantification of the performance recovery phenomenon at different STOP-SOC and battery age at 55 ◦ C. Aging days

22

37

51

Stop-SOC

20%

20%

20%

Capacity rise (%) due to recovery Capacity loss (%) Internal resistance rise (%)

0.4014 2.44 21.95

0.365 3.52 30.78

0.4175 4.32 38.73

58

65

91

134

152

180

Fully discharged 1.154 4.65 42.04

Fully discharged 0.985 5.04 45.66

Fully discharged 1.02 6.13 58.32

Fully discharged 1.09 7.78 77.24

Fully discharged 0.93 9.03 81.53

20%

From a previous study on lithium-ion batteries calendar aging [21], this was confirmed and effectively at constant temperature, the increase of SOC accelerated aging phenomenon such as SEI growth and loss of active material. That’s why keeping battery at a lower SOC could be helpful. Furthermore, from literature, the loss and/or consumption of recyclable lithium ions at the anode by the passive layer is considered as a major cause of the reduction in the reversible capacity of the lithium ion battery [22,23]. A discharging process during the storage prevents gas formation, from the decomposition of electrolyte, in the cell, and gives long-term stability to a flexible lithium battery [24]. When the battery is completely discharged, all lithium is completely extracted from the negative electrode and it is fully reincorporated in the positive electrode. So when charging, the lithium will be uniformly redistributed in the negative electrode. When battery is fully discharged, probably, a large amount of charge is preserved from the SEI-electrolyte interaction when they are in the positive electrode during rest time. However, note that recovery phenomenon does not affect internal resistance increase because electrolyte decomposition is mainly related to temperature impact. 3.3. Recovery phenomenon modeling 3.3.1. Power cycling aging modeling The square root of aging time function given in Eq. (5) refers in the literature to the dynamic evolution of the negative electrode layer SEI, responsible of the insertion and de-insertion of lithium ions into graphite [25–27]. As described before, this phenomenon is mentioned as a major source of aging. The behavior of batteries at 55 ◦ C confirmed this. However, for aging test at 45 ◦ C, a linear evolution revealed to be efficient like discussed in the end of Section 3.1). (C, R)(t) = At

0.5

+B

0.46 11.45 101.37

Actually, accuracy of the model has been tested in real-time. For this, measured performances from experiments after 12 aging days are compared to those simulated using the model developed previously. Relative error in these points was little; it was about 0.11% for capacity and 1.25% for resistance. This error is calculated using next equation, Err =

(|Yexp − Ymodel |) Yexp

(6)

with Yexp the experimental performance (capacity or resistance) and Ymodel the simulated performance. 3.3.2. Recovery modeling For the combined-aging mode modeling, first of all, a global function similar to this use in the scenario 1 is used to represent capacity range evolution through time. Results showed that model follows globally the capacity evolution. However, it was not able to take into account recovery phenomenon and so this model neglects the capacity rise after stop (see Fig. 12 for test running at 55 ◦ C and Fig. 10 for test conducted at 45 ◦ C). Therefore, this global form is used to estimate capacity just before the pause and a corrective term is added in the model formulation. This term takes into account the capacity increase related to recovery phenomenon after the rest-time (pause) according to the Stop-SOC and will serve for capacity estimation after the pause. Moreover, like battery capacity degradation within the week behaved similarly, this behavior served for estimating capacity within the week for the rest of the aging time. Experimental data confirmed a good fitting accuracy with a polynomial function decried with expression (9). Cafter = Crise (Stop-SOC) + Cbefore

(7)

Cbefore = Ct 0.5 + D

(8)

Cwithin = Cafter (at 2 + bt + c)

(9)

(5) a = 0.0003;

This change in aging behavior, which is mainly related to thermal effect, has been discussed before by Belt et al. [28] which focused on battery aging and confirmed that battery performances evolution can be linear or follow the square root of the aging time according to temperature range. This can be related to the fact that temperature influences mainly the electrolyte decomposition and the growth of the SEI layer [4]. Table 3 presents the identified model parameters from the power-cycling aging test at 55 ◦ C. Fig. 11 shows a comparison between the measured and the model-simulated capacity (a) and resistance (b).

b = −0.0033;

c = 1;

C = −0.088;

D = 13.8163 (10)

with Crise (Stop-SOC) is equal to 0.95% of initial capacity if battery is fully discharged and 0.45% of initial capacity for the SOC 20% and Table 3 Capacity and resistance model parameters. Model/parameters

A

B

Capacity Resistance

−0.1278 0.2207

13.7424 2.0683

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4

13.7 Model Measured Measured @ 55 5days

13.6

Internal resistance (m Ω)

13.5 13.4 Capacity (Ah)

755

13.3 13.2 13.1 13

3.5

3

2.5 Model Measured Measured @ 55day d s

12.9 12.8 12.7

2 0

10

20

30 Aging days

40

50

60

0

10

20

30 Aging days

40

50 0

60

Fig. 11. Capacity fade (a) and internal resistance increase (b) modeling at 55 ◦ C.

13.8 Model Experiment

13.7

Capacity (Ah)

13.6 13.5 13.4 13.3 13.2 13.1

Fig. 12. Capacity fade modeling with a global model for the battery aged with combined mode at 55 ◦ C.

0

10

20 30 Aging days

40

Fig. 13. Consideration of recovery phenomenon in capacity fade modeling for battery aged with combined mode at 55 ◦ C.

SOC10%. These Crise values are calculated based on the average of several weeks of tests. Results from the comprehensive model simulation are presented in Fig. 13. Relative modeling error presented in Fig. 14 confirmed the good accuracy of the model. It does not exceed 0.4%.

-3

x 10

4 3.5 3

Like capacity recovery phenomenon is strongly related to rest time, the kinetic of charge redistribution is very important. Therefore, in this part, a short view on the rest-time duration influence on recovery phenomenon is given. Basically, from the beginning, the rest-time for the scenario 2 is fixed to 48 h. However, in the last weeks, this duration is modified to 25 h, 12 h, 6 h, 4 h and 2 h, respectively. Table 4 represents obtained results from the test running under 45 ◦ C. Table 4 Capacity recovery quantification from different rest-time duration for the batteries aged at 45 ◦ C and Stop-SOC 0%. Rest time duration

48 h

25 h

12 h

6h

4h

2h

Crise (%)

1.07

1.02

0.975

0.95

0.62

0.12

Realative error (%)

3.4. Rest-time duration impact on recovery phenomenon

2.5 2 1.5 1 0.5 0

0

5

10

15

20 25 Aging days

30

35

40

45

Fig. 14. Relative error from comprehensive capacity fade modeling for battery aged with combined mode at 55 ◦ C.

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Table 5 Power-cycling aging quantification from the two temperatures. Test temperature/aging days

10 (%)

30 (%)

44 (%)

100 (%)

150 (%)

178 (%)

45 ◦ C

0.23 4.66 1.98 27.66

1.24 10.02 4.38 50.08

2.07 16.27 5.36 59.13

5.23 31.99 9.1 97.59

8.03 46.44 12.17 161.82

8.68 9.95 12.8 14.96 59.22 65.68 79.55 87.39 Test is stopped because R is higher than 100%

1.75

90

1.5

85

1.25

80

50

100

150 200 Aging days

250

300

1 350

257 (%)

2

95

1.75

90

1.5

85

1.25

80

301

0

50

100

Normalized resistance (PU)

95

200 (%)

100

2

Relative capacity (%)

Relative capacity (%)

100

Normalized resistance (PU)

55 ◦ C

Capacity loss C Resistance rise R Capacity loss C Resistance rise R

1 150

Aging days

Fig. 15. Temperature impact on power cycling aging.

Results show that capacity rise is slightly impacted with resttime duration above the threshold of 6 h. Furthermore, lower than of around 2 h of rest-time the relaxation does not affect battery capacity. 4. Temperature impact on lithium-ion power-cycling From a short-term point of view, capacity recovery helps for performance improvement and hence a higher energy and therefore higher autonomy for the vehicle is obtained. However, for longterm aspect, battery life depends on both its capacity and also its internal resistance. For lithium-batteries, criteria fixed for end-of-life or more recently criteria for moving to second life, as well known, are 20% of capacity loss and 100% of impedance rise. These criteria are generally fixed by the manufacturers and basically over these performances battery could not fulfill requirement and power demand. Thus, keeping battery at a fully discharged state at rest is a potential source for prolonging battery life if temperature constraint is not extreme and hence if the battery impedance does not increase rapidly compared to capacity loss (that means battery behavior is stable with temperature [29,30]). Therefore, temperature effect is presented and its impact on power-cycling aging is discussed in this section. Table 5 shows a quantification of the continuous power-cycling aging (scenario 1) in terms of internal resistance rise and capacity loss for the two experiments, respectively 45 ◦ C and 55 ◦ C, at several life-time. This table points out that rise in aging temperature influences mainly the internal resistance increase. For example, capacity loss reaches 5.36% after only 44 days at 55 ◦ C compared to 102 days at 45 ◦ C. However, internal resistance increase reaches 59% after 44 days compared to 177 days at 45 ◦ C. In fact, temperature rise of 10 ◦ C strengthen the aging by over 158% according to capacity loss and 263% according to impedance rise after 44 aging days. Fig. 15 illustrates clearly this change in aging behavior which is due to temperature factor. In fact, 100% of internal resistance is reached near 100 aging days for battery aged at 55 ◦ C compared

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