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Calendar and cycling ageing combination of batteries in electric vehicles
Microelectronics Reliability 88–90 (2018) 1212–1215
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
Calendar and cycling ageing combination of batteries in electric vehicles a,*
b
Eduardo Redondo-Iglesias , Pascal Venet , Serge Pelissier a b
T
a
Univ Lyon, IFSTTAR/AME/LTE, Bron 69500, France Univ Lyon, Université Lyon 1, AMPERE UMR CNRS 5005, Villeurbanne 69100, France
A R T I C LE I N FO
A B S T R A C T
Keywords: Lithium-ion battery Accelerated ageing tests Calendar ageing Cycling ageing
The battery is the most sensitive part in the powertrain of full electric vehicles because of its cost and weight. The full electric vehicle range and prize are highly determined by the battery performances. During its lifespan, the battery performances are degraded because of ageing mechanisms. Typically, there are two types of ageing: calendar and cycling ageing. In the electric vehicle application, both types of ageing coexist and interact. In this paper, we report the results of accelerated ageing tests and present a methodology to separate calendar from cycling ageing. With this analysis method, we demonstrate the interaction between calendar and cycling ageing when battery is cycled following representative current profiles of the electric vehicle application.
1. Introduction Studying the ageing of batteries is necessary because the degradation of their features largely determines the cost, the performances and the environmental impact of electric vehicles, particularly of full electric vehicles (EVs). In this type of studies, battery ageing is typically classified in two types: calendar and cycling ageing. Calendar ageing occurs when a battery is at rest condition; this is when no current flows through the battery. Cycling ageing occurs when the battery is charged or discharged. Battery ageing lies on ageing mechanisms. These mechanisms are parasitic physicochemical transformations degrading energy (capacity) and power (impedance) capabilities of the battery. In modern lithium-ion batteries, the main calendar ageing mechanism is the formation and growth of the Solid Electrolyte Interface (SEI) layer on the negative (graphite) electrode [1,2]. SEI formation is accelerated at high levels of temperature and State of Charge (SoC) [3]. The main cycling ageing mechanism is the lithium plating of the negative electrode. This mechanism is increased at high charge rates and at low temperatures [4,5]. Other cycling ageing mechanisms are, for example, particle cracking and collector corrosion. However, this type of mechanisms occurs mostly in extreme use conditions at very high current rates or very deep discharges, not in normal use conditions [3]. Electric cars spend most of the time (90% or more) parked and current rates of the battery are relatively low when they are used. In EV applications the average current rates are around C/5 with maximum
*
values around 3C. Except for fast charge, the main ageing mechanism in this application should be SEI formation and growth. Battery ageing is path dependent, specially when the battery is submitted to a series of high power cycling and calendar periods. This is the case for example of hybrid electric vehicles (HEV). In EVs, batteries are used at relatively low power (compared to HEV). For this reason, cycling ageing is frequently neglected (e.g. [6]). Some authors assume linear [7,8]) or non-linear [9] superposition between calendar and cycling ageing, but in both cases path dependence is not taken into account, i.e. calendar-cycling superposition is supposed invariant over time. Typically, battery ageing tests campaigns consist in calendar ageing tests [2,10], and cycling ageing tests [11,12] or both [13-16]. The purpose of this paper is to present an ageing test campaign demonstrating the non-linearity of calendar and cycling ageing superposition in the EV application. The originality of this work is to consider cycling tests composed of soft and short charging/discharging periods followed by relatively long rest periods. The cycling current rates are very low compared to maximum allowed current rates by manufacturer in order to exclude typical cycling mechanisms (lithium plating, particle cracking, etc.). 2. Experimental setup 2.1. Starting hypotheses The aim of the experimental campaign is to better understand the influence of calendar and cycling in electric vehicles applications.
Corresponding author. E-mail address: [email protected] (E. Redondo-Iglesias).
https://doi.org/10.1016/j.microrel.2018.06.113 Received 29 May 2018; Received in revised form 30 June 2018; Accepted 30 June 2018 0026-2714/ © 2018 Elsevier Ltd. All rights reserved.
Microelectronics Reliability 88–90 (2018) 1212–1215
E. Redondo-Iglesias et al.
The main assumption here is to consider SEI formation and growth as unique ageing mechanism. This assumption leads to the three hypotheses: (i) Cycling induces no direct ageing. (ii) Degradation is mainly due to calendar ageing. (iii) Cycling accelerates the calendar ageing. When cycling is performed at very low current rates, ageing mechanisms as lithium plating, particle cracking, etc. are negligible related to SEI (hypotheses i and ii). Nevertheless, cycling induces volume and concentration changes in both electrodes independently of current rate. Hypothesis iii means that during the rest period after being cycled, a battery can be predisposed to suffer more degradation (transient calendar ageing acceleration after cycling). 2.2. Accelerated ageing tests Accelerated ageing tests were conducted on 36 lithium-ion cells from KOKAM (model number SLPB283452H, nominal capacity 0.35 Ah, NMC/graphite) at 60 °C. Cells' performances were periodically measured by the means of Reference Performance Tests (RPT). The RPT consisted in measuring the self-discharge and the cell capacity at two current rates (1C and C/ 10). Calendar ageing consisted in leaving the cells at rest condition (disconnected) at 5 different SoC levels: 100, 90, 80, 70 and 50%. Three cells were tested at each SoC level to confirm the results repeatability. Cycling ageing consisted in periodically charge and discharge the cells following 7 different profiles (Fig. 1). Three cells were tested at each profile to confirm the results repeatability. The first profile (profile 1) is inspired by the tests performed by [17]. It represents a daily round-trip with a full charge (CCCV) at the end of the day. Profile 2 differs from profile 1 in the full charge. In this case the full charge (CCCV) is performed only 1 day a week, all the other days of the week battery is charged following a CC charge (not fully charged). Profile 3 differs from profile 1 in the return trip. In this profile the cell is only discharged once a day. Profiles 4 and 5 are accelerated versions of profile 3. Rest times were calculated to change the period from 24 h to 6 and 3 h (profile 4 and 5 respectively). Finally, profiles 6 and 7 alternate two SoC levels: 90–70% for profile 6 and 70–50% for profile 7. In this work, cycling ageing is performed at very low current rates (C/2 in discharge, C/5 in charge) compared to maximum allowed rates indicated by the battery manufacturer (20C in discharge, 2C in charge). These current levels are representative of those for EV applications as indicated in Section 1. To ensure representativeness of the cycling profiles, battery cells are in rest condition most of the time, as in the EV application. For example in profiles 1 and 2 the cells are charged/discharged during less than 3 h a day. In the following sections the obtained results of cycling and calendar ageing will be presented and analysed. 3. Experimental results
Fig. 1. Cycling profiles. Profile 2 is similar to profile 1 once a week (CCCV charge), six days a week charge is performed with CC (without CV phase).
Fig. 2 shows the results of calendar ageing tests. This figure is a median/max/min plot of QF measured values for each SoC level at RPT tests (three cells per SoC level). Two groups are well differentiated: in SoC90 and SoC100 cells, capacity fade evolution is mainly linear; while in cells with SoC under 90% this evolution has a shape similar to a square root of time shape. The dispersion of calendar ageing results is quite high for the first group (SoC100 and SoC90 cells); specially at SoC = 100%: after 120 days (last RPT) the maximum, median and
minimum values of QF are 0.273, 0.258 and 0.191 p.u. of initial capacity, i.e. a dispersion of 0.082 p.u. Fig. 3 shows the results of the cycling ageing tests. As for the preceding figure, the median/max/min measured values of QF at RPT tests (three cells per cycling profile) are shown. As well as for calendar ageing, in this type of tests two groups are 1213
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E. Redondo-Iglesias et al.
4. Analysis of results The purpose of this section is to analyse the influence of each factor in calendar and cycling ageing. For calendar ageing, ageing factors are temperature T and SoC. For cycling, ageing factors are T, SoC and current I. Furthermore, each factor contribution cannot be simply added, interactions between factors may exist. Fig. 2 reveals that the degradation during calendar ageing is clearly non-linear. The cycling ageing results (Fig. 3) are more difficult to analyse because of the number of factors and their interactions. Moreover, during these tests, calendar ageing is overlapped with cycling. Therefore, we need to separate the calendar part of ageing in the cycling ageing. For this, we will use a cumulative damage approach. This method relies on two hypotheses: i) The damage produced by one event is the same independently of the past events. ii) The damage produced by an event is independent of other events.
Fig. 2. Capacity fade under calendar ageing conditions. Relative values of capacity fade are expressed in p.u. which means “per unit”.
Cumulative damage approach is typically used to determine the lifetime of a device as a consequence of successive events. In this work, we apply this method to capacity fade measurement decomposition. Capacity fade can be decomposed in two parts:
QF = QF , cal + QF , other
(1)
QF,cal represents the capacity fade exclusively due to calendar ageing (T, SoC). QF,other include all the other events leading to the battery ageing: discharges,charges, SoC changes, floating charges, etc. QF,cal is calculated from the results of calendar ageing tests. At each SoC level, QF,cal can be derived to obtain the irreversible leakage current [18]:
IF , cal (SoC , t ) = dQF , cal (SoC , t )/dt
(2)
Then, for each cell in cycling ageing, QF,cal is obtained by integrating IF,cal(SoC) over time. Finally, the cycling part of ageing, QF,other is obtained by subtracting QF,cal to the measured values of QF. Fig. 3. Capacity fade under cycling ageing. Relative values of capacity fade are expressed in p.u.
5. Discussion In Fig. 4, QF and its decomposition in calendar-cycling parts for each profile are illustrated. After 120 cycles, cells exposed to profiles 1 and 3 lost about 28% with a similar decomposition: 15% from calendar and 13% from cycling. Both profiles are quite similar (current rates, rest period at SoC100 and SoC80), but profile 3 does not contain the additional discharge of the return trip. Then, after 120 cycles, cells under profile 1 were cycled with the double Amp-hours (Ah) than cells under profile 3. This proves that cycling ageing cannot be simply calculated from Ah counting. The difference between profile 1 and profile 2 is that CCCV charges are performed every day in profile 1 and only once a week in profile 2. When analysing capacity fade, cells exposed to profile 2 were less degraded (25%, compared to 28% in profile 1) and this difference is
well differentiated. First group is formed by cells under profiles 1 to 3 and a second group is formed by the cells under profiles 6 and 7. The results for profiles 4 and 5 will be discussed below. The main difference between both groups is the SoC level. In the first group, cells are charged to SoC100 periodically while cells of the second group are charged at a maximum SoC of 90%. The dispersion of results is also very different between the two groups. In the first group (profiles 1, 2 and 3), dispersion was around 0.07 p.u. whereas in the second group (profiles 6 and 7) it never exceeded 0.03 p.u. As a conclusion of this part, we can say that two groups exist in both test types: fast and linear ageing with time (Group 1) and moderate square of time shaped ageing (Group 2). Group 1 corresponds to cells spending a significant part of their live in higher levels of SoC: 90 to 100%. For calendar tests, that is SoC100 and SoC90 cells; while in cycling tests this group corresponds to cells subjected to profiles 1 to 5. Group 2 corresponds to cells spending most of time out of those SoC levels (< 90%): cells SoC50 to SoC80 in calendar tests and cells subjected to profiles 6 and 7 in cycling ageing. These cells passed a minimal part of time at SoC 90 to 100% (only during RPT tests). Both groups have also differences in the results dispersion (more dispersion in Group 1). The reason is that ageing is dependent of SoC in a very non-linear way (maybe exponential of SoC). Any uncertainty in SoC level (measurement imprecisions, differences in real capacity versus nominal capacity, etc.) has a higher effect at higher SoC levels, specially near of 100%.
Fig. 4. Decomposition of QF after 120 cycles in two parts: calendar and cycling. 1214
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mainly in the cycling part: 8% and 13% for profiles 2 and 1 respectively. This indicates that CV phases of charging may be critical. The decomposition of QF in profiles 6 and 7 shows that cycling ageing is almost non-existent at lower SoC levels ageing was basically calendar. Finally, when comparing profiles 3, 4 and 5, the difference in these profiles is the cycle period: 24, 6 and 3 h respectively. So, 120 cycles represent 120, 30 and 15 days respectively, thus calendar part of ageing is smaller (15, 10 and 4% respectively). Cycling part of ageing is also different between profiles 3, 4 and 5: 13, 4 and 1% respectively. This result confirms that cycling ageing depends strongly of the rests phases. Every charge or discharge phase in profile 3 is followed by a longer rest time than in profiles 4 and 5. From this, we would conclude that cycling implies an acceleration of subsequent calendar phases. In this work, cycling tests consisted in very soft and short cycling periods (e.g. C/2 or C/5 during 0.4 to 2 h in profile 3) followed by long rest periods (e.g. 7 to 15 h in profile 3). In these conditions, the main ageing mechanism should be SEI formation and growth. In fact, cycling tests are here quasi-calendar tests: cells subjected to cycling spent about 90% of their time in rest state (disconnected). Currents rates are C/5 (charge) or C/2 (discharge), while these cells accept current rates up to 2C (charge) or 20C (discharge). Any cycling related mechanism (e.g. lithium plating and particle cracking) could not take place. Incremental capacity analysis (ICA) [19] could be carried on RPT results to validate the ageing mechanisms taking place on each ageing condition.
[2] A. Delaille, S. Grolleau, F. Duclaud, J. Bernard, R. Revel, S. Pelissier, E. RedondoIglesias, J.-M. Vinassa, A. Eddahech, C. Forgez, M. Kassem, S. Joly, D. Porcellato, P. Gyan, S. Bourlot, M. Ouattara-Brigaudet, SIMCAL project: calendar aging results obtained on a panel of 6 commercial Li-ion cells, ECS Meeting Abstracts, No. 14, The Electrochemical Society, San Francisco, 2013, p. 1191 http://ma.ecsdl.org/ content/MA2013-02/14/1191.full.pdf. [3] J. Vetter, P. Novák, M. Wagner, C. Veit, K.-C. Möller, J. Besenhard, M. Winter, M. Wohlfahrt-Mehrens, C. Vogler, A. Hammouche, Ageing mechanisms in lithiumion batteries, J. Power Sources 147 (1-2) (2005) 269–281 http://www. sciencedirect.com/science/article/pii/S0378775305000832https://doi.org/10. 1016/j.jpowsour.2005.01.006. [4] S. Tippmann, D. Walper, L. Balboa, B. Spier, W.G. Bessler, Low-temperature charging of lithium-ion cells part I: electrochemical modeling and experimental investigation of degradation behavior, J. 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Uzunoglu, A dynamic lithium-ion battery model considering the effects of temperature and capacity fading, 2009 International Conference On Clean Electrical Power, IEEE, 2009, pp. 383–386. [8] J. Schmalstieg, S. Käbitz, M. Ecker, D.U. Sauer, A holistic aging model for Li (NiMnCo)O2 based 18650 lithium-ion batteries, J. Power Sources 257 (0) (2014) 325–334 http://www.sciencedirect.com/science/article/pii/ S0378775314001876https://doi.org/10.1016/j.jpowsour.2014.02.012. [9] A. Millner, Modeling lithium ion battery degradation in electric vehicles, 2010 IEEE Conference On Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES), IEEE, 2010, pp. 349–356. [10] J. Schmitt, A. Maheshwari, M. Heck, S. Lux, M. Vetter, Impedance change and capacity fade of lithium nickel manganese cobalt oxide-based batteries during calendar aging, J. Power. 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Bloom, Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes, J. Power Sources 196 (23) (2011) 10213–10221 http://www.sciencedirect.com/science/ article/pii/S0378775311016107 https://doi.org/10.1016/j.jpowsour.2011.08.067 Calendar and cycle ageing of LMO-NMC-graphite cells. [14] M. Ecker, N. Nieto, S. Kábitz, J. Schmalstieg, H. Blanke, A. Warnecke, D.U. Sauer, Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries, J. Power Sources 248 (2014) 839–851 http://www.sciencedirect.com/science/ article/pii/S0378775313016510https://doi.org/10.1016/j.jpowsour.2013.09.143. [15] I. Baghdadi, O. Briat, J.-Y. Delétage, P. Gyan, J.-M. Vinassa, Lithium battery aging model based on dakin's degradation approach, J. Power Sources 325 (2016) 273–285 http://www.sciencedirect.com/science/article/pii/ S0378775316307388https://doi.org/10.1016/j.jpowsour.2016.06.036. [16] J. Jaguemont, L. Boulon, P. Venet, Y. Dubé, A. Sari, Lithium-ion battery aging experiments at subzero temperatures and model development for capacity fade estimation, IEEE Trans. Veh. Technol. 65 (6) (2016) 4328–4343, https://doi.org/10. 1109/TVT.2015.2473841. [17] S. Grolleau, A. Delaille, H. Gualous, Predicting lithium-ion battery degradation for efficient design and management, Electric Vehicle Symposium and Exhibition (EVS27), Barcelona, Spain, 2013, pp. 1–6, , https://doi.org/10.1109/EVS.2013. 6914799. [18] E. Redondo-Iglesias, P. Venet, S. Pelissier, Global model for self-discharge and capacity fade in lithium-ion batteries based on the generalized Eyring relationship, IEEE Trans. Veh. Technol. 67 (1) (2018) 104–113 https://ieeexplore.ieee.org/ document/8031363/https://doi.org/10.1109/TVT.2017.2751218. [19] M. Dubarry, C. Truchot, B.Y. Liaw, Synthesize battery degradation modes via a diagnostic and prognostic model, J. 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6. Conclusion Accelerated ageing tests were conducted on lithium-ion cells at 60 °C. The test campaign was designed to measure the influence of SoC level in calendar ageing and the interactions between cycling and calendar ageing in electric vehicle applications. In literature, cycling ageing is frequently neglected or superposed to calendar ageing without considering interactions between these two ageing modes. The cycling ageing tests consisted in soft and short cycling periods (current rates of C/2 to C/5 during 2 h maximum) followed by relatively long rest periods (7 to 15 h). In these conditions, cycling ageing mechanisms might be neglected compared with calendar one (SEI formation and growth). In a first approach, we use the cumulative damage to separate cycling and calendar parts of ageing. The obtained results confirm that cycling must not be neglected, moreover a very non-linear superposition of calendar and cycling exists, demonstrating the path dependence, even at very low current rates and very infrequent cycling periods (94% of time in rest condition). The results highlight that cycling and calendar ageing interact. Therefore, there is no simple way to model simultaneously calendar and cycling ageing. Next step will consist in confirm if SEI formation ad growth is the main mechanism by using ICA. Further work will consist in developing a combined ageing model suitable for this application. References [1] M. Kassem, C. Delacourt, Postmortem analysis of calendar-aged graphite/LiFePO4 cells, J. Power Sources 235 (2013) 159Ű-171 http://www.sciencedirect.com/ science/article/pii/S037877531300205Xhttps://doi.org/10.1016/j.jpowsour. 2013.01.147.
1215
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
Calendar and cycling ageing combination of batteries in electric vehicles a,*
b
Eduardo Redondo-Iglesias , Pascal Venet , Serge Pelissier a b
T
a
Univ Lyon, IFSTTAR/AME/LTE, Bron 69500, France Univ Lyon, Université Lyon 1, AMPERE UMR CNRS 5005, Villeurbanne 69100, France
A R T I C LE I N FO
A B S T R A C T
Keywords: Lithium-ion battery Accelerated ageing tests Calendar ageing Cycling ageing
The battery is the most sensitive part in the powertrain of full electric vehicles because of its cost and weight. The full electric vehicle range and prize are highly determined by the battery performances. During its lifespan, the battery performances are degraded because of ageing mechanisms. Typically, there are two types of ageing: calendar and cycling ageing. In the electric vehicle application, both types of ageing coexist and interact. In this paper, we report the results of accelerated ageing tests and present a methodology to separate calendar from cycling ageing. With this analysis method, we demonstrate the interaction between calendar and cycling ageing when battery is cycled following representative current profiles of the electric vehicle application.
1. Introduction Studying the ageing of batteries is necessary because the degradation of their features largely determines the cost, the performances and the environmental impact of electric vehicles, particularly of full electric vehicles (EVs). In this type of studies, battery ageing is typically classified in two types: calendar and cycling ageing. Calendar ageing occurs when a battery is at rest condition; this is when no current flows through the battery. Cycling ageing occurs when the battery is charged or discharged. Battery ageing lies on ageing mechanisms. These mechanisms are parasitic physicochemical transformations degrading energy (capacity) and power (impedance) capabilities of the battery. In modern lithium-ion batteries, the main calendar ageing mechanism is the formation and growth of the Solid Electrolyte Interface (SEI) layer on the negative (graphite) electrode [1,2]. SEI formation is accelerated at high levels of temperature and State of Charge (SoC) [3]. The main cycling ageing mechanism is the lithium plating of the negative electrode. This mechanism is increased at high charge rates and at low temperatures [4,5]. Other cycling ageing mechanisms are, for example, particle cracking and collector corrosion. However, this type of mechanisms occurs mostly in extreme use conditions at very high current rates or very deep discharges, not in normal use conditions [3]. Electric cars spend most of the time (90% or more) parked and current rates of the battery are relatively low when they are used. In EV applications the average current rates are around C/5 with maximum
*
values around 3C. Except for fast charge, the main ageing mechanism in this application should be SEI formation and growth. Battery ageing is path dependent, specially when the battery is submitted to a series of high power cycling and calendar periods. This is the case for example of hybrid electric vehicles (HEV). In EVs, batteries are used at relatively low power (compared to HEV). For this reason, cycling ageing is frequently neglected (e.g. [6]). Some authors assume linear [7,8]) or non-linear [9] superposition between calendar and cycling ageing, but in both cases path dependence is not taken into account, i.e. calendar-cycling superposition is supposed invariant over time. Typically, battery ageing tests campaigns consist in calendar ageing tests [2,10], and cycling ageing tests [11,12] or both [13-16]. The purpose of this paper is to present an ageing test campaign demonstrating the non-linearity of calendar and cycling ageing superposition in the EV application. The originality of this work is to consider cycling tests composed of soft and short charging/discharging periods followed by relatively long rest periods. The cycling current rates are very low compared to maximum allowed current rates by manufacturer in order to exclude typical cycling mechanisms (lithium plating, particle cracking, etc.). 2. Experimental setup 2.1. Starting hypotheses The aim of the experimental campaign is to better understand the influence of calendar and cycling in electric vehicles applications.
Corresponding author. E-mail address: [email protected] (E. Redondo-Iglesias).
https://doi.org/10.1016/j.microrel.2018.06.113 Received 29 May 2018; Received in revised form 30 June 2018; Accepted 30 June 2018 0026-2714/ © 2018 Elsevier Ltd. All rights reserved.
Microelectronics Reliability 88–90 (2018) 1212–1215
E. Redondo-Iglesias et al.
The main assumption here is to consider SEI formation and growth as unique ageing mechanism. This assumption leads to the three hypotheses: (i) Cycling induces no direct ageing. (ii) Degradation is mainly due to calendar ageing. (iii) Cycling accelerates the calendar ageing. When cycling is performed at very low current rates, ageing mechanisms as lithium plating, particle cracking, etc. are negligible related to SEI (hypotheses i and ii). Nevertheless, cycling induces volume and concentration changes in both electrodes independently of current rate. Hypothesis iii means that during the rest period after being cycled, a battery can be predisposed to suffer more degradation (transient calendar ageing acceleration after cycling). 2.2. Accelerated ageing tests Accelerated ageing tests were conducted on 36 lithium-ion cells from KOKAM (model number SLPB283452H, nominal capacity 0.35 Ah, NMC/graphite) at 60 °C. Cells' performances were periodically measured by the means of Reference Performance Tests (RPT). The RPT consisted in measuring the self-discharge and the cell capacity at two current rates (1C and C/ 10). Calendar ageing consisted in leaving the cells at rest condition (disconnected) at 5 different SoC levels: 100, 90, 80, 70 and 50%. Three cells were tested at each SoC level to confirm the results repeatability. Cycling ageing consisted in periodically charge and discharge the cells following 7 different profiles (Fig. 1). Three cells were tested at each profile to confirm the results repeatability. The first profile (profile 1) is inspired by the tests performed by [17]. It represents a daily round-trip with a full charge (CCCV) at the end of the day. Profile 2 differs from profile 1 in the full charge. In this case the full charge (CCCV) is performed only 1 day a week, all the other days of the week battery is charged following a CC charge (not fully charged). Profile 3 differs from profile 1 in the return trip. In this profile the cell is only discharged once a day. Profiles 4 and 5 are accelerated versions of profile 3. Rest times were calculated to change the period from 24 h to 6 and 3 h (profile 4 and 5 respectively). Finally, profiles 6 and 7 alternate two SoC levels: 90–70% for profile 6 and 70–50% for profile 7. In this work, cycling ageing is performed at very low current rates (C/2 in discharge, C/5 in charge) compared to maximum allowed rates indicated by the battery manufacturer (20C in discharge, 2C in charge). These current levels are representative of those for EV applications as indicated in Section 1. To ensure representativeness of the cycling profiles, battery cells are in rest condition most of the time, as in the EV application. For example in profiles 1 and 2 the cells are charged/discharged during less than 3 h a day. In the following sections the obtained results of cycling and calendar ageing will be presented and analysed. 3. Experimental results
Fig. 1. Cycling profiles. Profile 2 is similar to profile 1 once a week (CCCV charge), six days a week charge is performed with CC (without CV phase).
Fig. 2 shows the results of calendar ageing tests. This figure is a median/max/min plot of QF measured values for each SoC level at RPT tests (three cells per SoC level). Two groups are well differentiated: in SoC90 and SoC100 cells, capacity fade evolution is mainly linear; while in cells with SoC under 90% this evolution has a shape similar to a square root of time shape. The dispersion of calendar ageing results is quite high for the first group (SoC100 and SoC90 cells); specially at SoC = 100%: after 120 days (last RPT) the maximum, median and
minimum values of QF are 0.273, 0.258 and 0.191 p.u. of initial capacity, i.e. a dispersion of 0.082 p.u. Fig. 3 shows the results of the cycling ageing tests. As for the preceding figure, the median/max/min measured values of QF at RPT tests (three cells per cycling profile) are shown. As well as for calendar ageing, in this type of tests two groups are 1213
Microelectronics Reliability 88–90 (2018) 1212–1215
E. Redondo-Iglesias et al.
4. Analysis of results The purpose of this section is to analyse the influence of each factor in calendar and cycling ageing. For calendar ageing, ageing factors are temperature T and SoC. For cycling, ageing factors are T, SoC and current I. Furthermore, each factor contribution cannot be simply added, interactions between factors may exist. Fig. 2 reveals that the degradation during calendar ageing is clearly non-linear. The cycling ageing results (Fig. 3) are more difficult to analyse because of the number of factors and their interactions. Moreover, during these tests, calendar ageing is overlapped with cycling. Therefore, we need to separate the calendar part of ageing in the cycling ageing. For this, we will use a cumulative damage approach. This method relies on two hypotheses: i) The damage produced by one event is the same independently of the past events. ii) The damage produced by an event is independent of other events.
Fig. 2. Capacity fade under calendar ageing conditions. Relative values of capacity fade are expressed in p.u. which means “per unit”.
Cumulative damage approach is typically used to determine the lifetime of a device as a consequence of successive events. In this work, we apply this method to capacity fade measurement decomposition. Capacity fade can be decomposed in two parts:
QF = QF , cal + QF , other
(1)
QF,cal represents the capacity fade exclusively due to calendar ageing (T, SoC). QF,other include all the other events leading to the battery ageing: discharges,charges, SoC changes, floating charges, etc. QF,cal is calculated from the results of calendar ageing tests. At each SoC level, QF,cal can be derived to obtain the irreversible leakage current [18]:
IF , cal (SoC , t ) = dQF , cal (SoC , t )/dt
(2)
Then, for each cell in cycling ageing, QF,cal is obtained by integrating IF,cal(SoC) over time. Finally, the cycling part of ageing, QF,other is obtained by subtracting QF,cal to the measured values of QF. Fig. 3. Capacity fade under cycling ageing. Relative values of capacity fade are expressed in p.u.
5. Discussion In Fig. 4, QF and its decomposition in calendar-cycling parts for each profile are illustrated. After 120 cycles, cells exposed to profiles 1 and 3 lost about 28% with a similar decomposition: 15% from calendar and 13% from cycling. Both profiles are quite similar (current rates, rest period at SoC100 and SoC80), but profile 3 does not contain the additional discharge of the return trip. Then, after 120 cycles, cells under profile 1 were cycled with the double Amp-hours (Ah) than cells under profile 3. This proves that cycling ageing cannot be simply calculated from Ah counting. The difference between profile 1 and profile 2 is that CCCV charges are performed every day in profile 1 and only once a week in profile 2. When analysing capacity fade, cells exposed to profile 2 were less degraded (25%, compared to 28% in profile 1) and this difference is
well differentiated. First group is formed by cells under profiles 1 to 3 and a second group is formed by the cells under profiles 6 and 7. The results for profiles 4 and 5 will be discussed below. The main difference between both groups is the SoC level. In the first group, cells are charged to SoC100 periodically while cells of the second group are charged at a maximum SoC of 90%. The dispersion of results is also very different between the two groups. In the first group (profiles 1, 2 and 3), dispersion was around 0.07 p.u. whereas in the second group (profiles 6 and 7) it never exceeded 0.03 p.u. As a conclusion of this part, we can say that two groups exist in both test types: fast and linear ageing with time (Group 1) and moderate square of time shaped ageing (Group 2). Group 1 corresponds to cells spending a significant part of their live in higher levels of SoC: 90 to 100%. For calendar tests, that is SoC100 and SoC90 cells; while in cycling tests this group corresponds to cells subjected to profiles 1 to 5. Group 2 corresponds to cells spending most of time out of those SoC levels (< 90%): cells SoC50 to SoC80 in calendar tests and cells subjected to profiles 6 and 7 in cycling ageing. These cells passed a minimal part of time at SoC 90 to 100% (only during RPT tests). Both groups have also differences in the results dispersion (more dispersion in Group 1). The reason is that ageing is dependent of SoC in a very non-linear way (maybe exponential of SoC). Any uncertainty in SoC level (measurement imprecisions, differences in real capacity versus nominal capacity, etc.) has a higher effect at higher SoC levels, specially near of 100%.
Fig. 4. Decomposition of QF after 120 cycles in two parts: calendar and cycling. 1214
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mainly in the cycling part: 8% and 13% for profiles 2 and 1 respectively. This indicates that CV phases of charging may be critical. The decomposition of QF in profiles 6 and 7 shows that cycling ageing is almost non-existent at lower SoC levels ageing was basically calendar. Finally, when comparing profiles 3, 4 and 5, the difference in these profiles is the cycle period: 24, 6 and 3 h respectively. So, 120 cycles represent 120, 30 and 15 days respectively, thus calendar part of ageing is smaller (15, 10 and 4% respectively). Cycling part of ageing is also different between profiles 3, 4 and 5: 13, 4 and 1% respectively. This result confirms that cycling ageing depends strongly of the rests phases. Every charge or discharge phase in profile 3 is followed by a longer rest time than in profiles 4 and 5. From this, we would conclude that cycling implies an acceleration of subsequent calendar phases. In this work, cycling tests consisted in very soft and short cycling periods (e.g. C/2 or C/5 during 0.4 to 2 h in profile 3) followed by long rest periods (e.g. 7 to 15 h in profile 3). In these conditions, the main ageing mechanism should be SEI formation and growth. In fact, cycling tests are here quasi-calendar tests: cells subjected to cycling spent about 90% of their time in rest state (disconnected). Currents rates are C/5 (charge) or C/2 (discharge), while these cells accept current rates up to 2C (charge) or 20C (discharge). Any cycling related mechanism (e.g. lithium plating and particle cracking) could not take place. Incremental capacity analysis (ICA) [19] could be carried on RPT results to validate the ageing mechanisms taking place on each ageing condition.
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6. Conclusion Accelerated ageing tests were conducted on lithium-ion cells at 60 °C. The test campaign was designed to measure the influence of SoC level in calendar ageing and the interactions between cycling and calendar ageing in electric vehicle applications. In literature, cycling ageing is frequently neglected or superposed to calendar ageing without considering interactions between these two ageing modes. The cycling ageing tests consisted in soft and short cycling periods (current rates of C/2 to C/5 during 2 h maximum) followed by relatively long rest periods (7 to 15 h). In these conditions, cycling ageing mechanisms might be neglected compared with calendar one (SEI formation and growth). In a first approach, we use the cumulative damage to separate cycling and calendar parts of ageing. The obtained results confirm that cycling must not be neglected, moreover a very non-linear superposition of calendar and cycling exists, demonstrating the path dependence, even at very low current rates and very infrequent cycling periods (94% of time in rest condition). The results highlight that cycling and calendar ageing interact. Therefore, there is no simple way to model simultaneously calendar and cycling ageing. Next step will consist in confirm if SEI formation ad growth is the main mechanism by using ICA. Further work will consist in developing a combined ageing model suitable for this application. References [1] M. Kassem, C. Delacourt, Postmortem analysis of calendar-aged graphite/LiFePO4 cells, J. Power Sources 235 (2013) 159Ű-171 http://www.sciencedirect.com/ science/article/pii/S037877531300205Xhttps://doi.org/10.1016/j.jpowsour. 2013.01.147.
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