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Capacity fade curves of lab-made pouch cells at different temperatures and C-rates; (a) 0.2C; (b) 0.5C; (c) 1C. The arrows show trends of increasing aging rates.
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Li-ion batteries show a minimum of their aging rate at a certain temperature. This minimum in the corresponding Arrhenius plot expresses the longest cycle life at a certain C-rate. By characterizing aging of laboratory-made pouch cells and commercial 21700 cells as a function of C-rate and ambient temperature, we confirm that this minimum indeed sh...
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On the way to a Precise Battery, the generation of measurement results and findings based on them play an important role. Although cycle life tests are time-consuming and expensive, they can provide support and important information. Especially in the current topic of accelerating the charging process, it is important to know how different charging...
Citations
... In contrast, the study by [34] indicated that an improved adaptive extended Kalman filter can accomplish co-estimation for battery capacity and SoC and is proven, ensuring that the SoC error remains within ±1.2% during the initial 50 seconds, and the relative capacity error is restricted to within 2% after convergence. However, the study solely considered the hysteresis effect, and complexity of driving conditions and overlooked the potential challenges that the battery encounters during prolonged exposure to high C-rates and aging as shown by the studies in [35], [36]. On the other hand, a search of the literature revealed that limited studies have focused on machine learning (ML) techniques due to the complexity in model-based [37], [38]. ...
The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.
... For a similar cell type, we found that at 25°C, i.e. close to RT, the transition of the temperature dependent dominant aging mechanism from Li plating at low temperatures to SEI growth a high temperature occurs. 46,48 Thus, even small variations in the aging temperature might already trigger Li deposition as a more severe and therefore dominant aging mechanism, which could explain the large variations in the aging curves. ...
It is known that both the material used in Li-ion battery cells, as well as their aging history and state of charge (SOC), strongly impact the safety of such cells. This study investigates the safety characteristics of new or aged 21700 cells containing silicon-graphite blend anodes together with Ni-rich NMC cathodes by accelerating rate calorimetry (ARC) at different SOC. Cells underwent cyclic aging at 0 °C, room temperature, or 50 °C to induce different aging mechanisms including Li plating and solid electrolyte interphase growth. The quasi-adiabatic heat-wait-seek ARC tests show lower temperatures for self-heating (SH), CID triggering, venting, and thermal runaway (TR) with increasing SOC, indicating reduced safety levels. Furthermore, the mass loss and TR intensity increase as the SOC of the cell increases. Aged cells show a similar SOC dependence as new cells in view of venting and TR, although both temperatures are reduced. The onset of SH at around 35 °C, independent of SOC, reveals a significant safety issue in cells with Li plating. Additional cell voltage monitoring and on-line mass spectrometry provide further insights into the decomposition processes. Our findings provide essential knowledge to improve the safety and design of Li-ion battery cells by identifying unsafe states.
... 39,40 Evaluating the aging rates in an Arrhenius plot often leads to a V-shape curve, with an optimum temperature corresponding to the longest cycle life at the minimum. [41][42][43][44][45][46] While the temperature dependent aging of Li-ion batteries with graphite anodes is well understood, 41,[43][44][45][46][47] the temperature dependent degradation mechanisms of Si-containing anodes are less clear and are barely studied so far. 29,30,32,38,[48][49][50] This applies particularly for Sigraphite composite anodes at temperatures in the range above and below room temperature. ...
... 39,40 Evaluating the aging rates in an Arrhenius plot often leads to a V-shape curve, with an optimum temperature corresponding to the longest cycle life at the minimum. [41][42][43][44][45][46] While the temperature dependent aging of Li-ion batteries with graphite anodes is well understood, 41,[43][44][45][46][47] the temperature dependent degradation mechanisms of Si-containing anodes are less clear and are barely studied so far. 29,30,32,38,[48][49][50] This applies particularly for Sigraphite composite anodes at temperatures in the range above and below room temperature. ...
... This optimum temperature corresponds to the transition from Li plating at low temperatures to accelerated SEI growth at high temperatures as the dominant aging mechanism, representing the lowest temperature without Li plating. [41][42][43][44]46 The maximum number of observed cycles until the EOL varies significantly by almost a factor of 20 between the cells with a pure graphite anode and those with SiG20.8 anode. While the SiG20.8 cells reached EOL in less than 200 cycles at the optimum temperature of 25°C, graphite cells maintained an SOH of 85 % after 3,000 cycles at the optimum temperature of 30°C. ...
Competing effects of graphite and Si result in a complex temperature dependent performance and degradation of Li-ion batteries with Si-graphite composite anodes. This study examines the influence of varying the Si content (0 to 20.8 wt%) in Si-graphite composite anodes with consistent areal capacity and N/P ratio in full cells containing NMC622 cathodes. One hundred pilot-scale double-layer pouch cells were built and cycle aged in the temperature range from −10 to 55 °C. Electrochemical characterization demonstrated that increasing Si contents enhance capacity and mitigate internal resistance at low temperatures. On the other hand, high Si contents decrease charge-discharge energy efficiency and cycle life, particularly at elevated temperatures. Post-mortem analysis of aged electrodes, including physico-chemical characterization (scanning electron microscopy, energy-dispersive X-ray analysis, thickness measurements) and cell reconstruction revealed significant solid electrolyte interphase growth and increased loss of active material in anodes with high Si content. The optimum temperature for longest cycle life as derived from Arrhenius plots decreased from 30 °C for graphite anodes to 10 °C for cells with moderate Si content up to 5.8 wt%. These findings allow the design of optimized cells by balancing the Si content versus operating temperature in order to achieve lowest cell aging.
... Unlike power batteries used in EVs, which have instantaneous high-power requirements (C R > 1 h −1 ), most battery systems in stationary applications are designed to normally operate at a low C-rate (C R < 0.25 h −1 ) with an instantaneous high-power output mode at C R < 1 h −1 for safety and service life extension [44][45][46]. For example, the grid-scale Li-ion battery (100 MW, 129 MWh from Tesla and Neoen) was installed in Hornsdale, South Australia, in 2017 [10,11]. ...
... Arrhenius behavior has been observed in Li-ion batteries [60,65,66], showing a minimum aging rate at a certain temperature. This minimum in the Arrhenius plot with a certain C-rate (T = 5-25 • C and C R < 0.2 h −1 ) points to the longest cycle life of Li-ion batteries [33,44]. For the discussed battery system, the aforementioned operating temperature and C-rate are close to the minimum in the corresponding Arrhenius plot, indicating a slower aging rate and longer life of the battery system. ...
Energy supply on high mountains remains an open issue since grid connection is not feasible. In the past, diesel generators with lead–acid battery energy storage systems (ESSs) were applied in most cases. Recently, photovoltaic (PV) systems with lithium-ion (Li-ion) battery ESSs have become suitable for solving this problem in a greener way. In 2016, an off-grid PV system with a Li-ion battery ESS was installed in Paiyun Lodge on Mt. Jade (the highest lodge in Taiwan). After operating for more than 7 years, the aging of the whole electric power system became a critical issue for its long-term usage. In this work, a method is established for analyzing the massive energy data (over 7 million rows), such as daily operation patterns, as well as the C-rate, temperature, and accumulated energy distributions, and estimating the health of the Li-ion battery system. A completed electric power improvement project dealing with power system aging is reported. Based on the long-term usage experience, a simple cost analysis model comparing lead–acid and Li-ion battery systems is built, revealing that expensive Li-ion batteries can compete with cheap lead–acid batteries for long-term usage on high mountains. This case study can provide engineers and researchers with a fundamental understanding of the long-term usage of off-grid PV ESSs and engineering on high mountains.
... All the methods require advanced knowledge and thus are not off-theshelf solutions for BMS manufacturers or other use cases. Attempts to simplify the complex process of battery cell aging or discern between different modes of aging generally thus far have included the use of differential voltage analysis, incremental capacity analysis [45], or a wider analysis of the capacity fade curves [13,46,47] typically to discern between aging mechanisms (e.g., LLI or LAM [48]). ...
As lithium-ion battery cells age through successive cycling, accurate determination of their state of health (SoH) becomes increasingly challenging and usually requires knowledge of initial capacity and storing large amounts of data from charge–discharge cycling. This study addresses the challenge by investigating the potential utility of voltage hysteresis as a measure of battery cell’s SoH. Since voltage hysteresis can be obtained from any single charge–discharge measurement, our approach could facilitate SoH determination by eliminating the need to store a large amount of data, instead inferring the SoH directly from the voltage hysteresis. By conducting simple capacity fade measurements at 1 C with intermittent rate capability measurements (from 0.1 C up to 5 C), we were able to effectively gather voltage hysteresis data during SoH evolution of the cell at five to six different C-rates. A Buttler-Volmer-like dependency between voltage hysteresis and charge/discharge rate and ultimately SoH is demonstrated for lab-assembled LiFePO4 and LiNi0.8Co0.1Mn0.1O2 half-cells. Furthermore, a direct association between SoH and voltage hysteresis emerges from the data. A linear dependency was observed for SoH > 70%, whereas for the full range SoH the correlation was better described by an exponential function. These findings underscore the potential of the simple voltage hysteresis measurement to serve as an indicator of battery health and highlight its utility under slow and fast charging conditions. This is an analytical approach that balances between the black-box approach to data analysis and the development of elaborate analytical models. This method offers the advantage of streamlining the SoH assessment process for potential second-life applications and facilitating more efficient battery management strategies.
... 19,23,24 For this approach, a linearization of the aging rate is assumed. 18 However, many factors such as the C-Rate, 23,25,26 electrode thickness or energy density 23,25,26 and the cell chemistry 27 impact the dominant aging mechanism, which then dictates the temperature dependency. [25][26][27] Furthermore, the dominant aging mechanism can change over the lifetime. ...
... 19,23,24 For this approach, a linearization of the aging rate is assumed. 18 However, many factors such as the C-Rate, 23,25,26 electrode thickness or energy density 23,25,26 and the cell chemistry 27 impact the dominant aging mechanism, which then dictates the temperature dependency. [25][26][27] Furthermore, the dominant aging mechanism can change over the lifetime. ...
... 18 However, many factors such as the C-Rate, 23,25,26 electrode thickness or energy density 23,25,26 and the cell chemistry 27 impact the dominant aging mechanism, which then dictates the temperature dependency. [25][26][27] Furthermore, the dominant aging mechanism can change over the lifetime. 28,29 For nonlinear capacity fade Kucinskis et al. 25 have proposed linearization of certain State of Health (SoH) regions, which can be visualized in a 2D aging color map. ...
In semi-empirical aging modeling of lithium ion-batteries an Arrhenius approach is commonly applied to describe the temperature dependency of a linear capacity loss. However, this dependency can change with degradation modes which was also observed in this cyclic aging study on NMC111-LMO graphite pouch cells in a temperature range of 4 °C to 48 °C. By means of differential voltage analysis and post-mortem analysis we correlated different regimes in capacity loss to degradation modes and aging mechanisms. In the first regime, a power dependency of time was observed. A second accelerated linear regime which followed an increase in loss of active material of the positive electrode was seen for medium (∼19 °C to 25 °C) to high aging temperatures. Transition metal dissolution was suggested to cause accelerated SEI growth. An activation energy could be estimated to 0.83 eV (± 0.17 eV, 95% CI). Finally, at aging temperatures around 45 °C we propose decreased charge transfer kinetics to result in mossy dendrites on the negative electrode which cause a final knee in aging trajectory. The findings highlight the necessity of sufficient aging temperatures and testing time.
... 42 A different behavior reported in literature is for example the temperature dependent transition from SEI growth at high temperatures to Li plating at low temperatures under cycling aging conditions, leading to a slope change in the Arrhenius plot. 16,41,[43][44][45][46] The only exception from the Arrhenius behavior in the present study on calendar aging is the fit for the SiG5.8 cells aged at 30% SoC. However, the deviating correlation coefficient is again caused by the unusually fast capacity loss of the cells aged at 45°C discussed earlier (see yellow data points in Fig. S2c) and should therefore not be interpreted as a contradiction to the otherwise assumed Arrhenius-like behavior. ...
Calendar aging of Li-ion batteries with Si/graphite electrodes was investigated within this study. A total of 121 single-layer pouch full cells with either graphite or Si/graphite (3.0 wt−%, 5.8 wt−% and 20.8 wt−% Si) anodes and NMC622 cathodes with the same N/P ratio were built on pilot-scale. Calendar aging was studied at SoC 30%, 60%, and 100%, as well as temperature (25 °C, 45 °C, 60 °C) and time dependence. The aging data was analyzed in terms of capacity fade and a square-root behavior was observed. Differential voltage analysis (DVA) has been performed as a function of aging time. The observed temperature and time dependence is best described by time dependent, 3D Arrhenius plots. Post-Mortem analysis (SEM, EDX, GD-OES) is applied to investigate the changes on electrode and material level. Conclusions are drawn on the main aging mechanisms for calendar aging of Li-ion cells with Si/graphite anodes and differences between Si/graphite and pure graphite anodes are discussed. The Si-containing cells show a combination of lithium inventory loss and a loss of accessible Si active material, both caused by SEI growth.
... With regard to the evolution of the electrolyte decomposition layers, elevated temperature is a very critical parameter believed to have great influence on SEI/CEI and is known to accelerate LIB aging [37,25,38,24,39,40] . On graphite electrodes, the SEI is assumed to grow thicker or have a different composition [38,39] . ...
X-ray photoelectron spectroscopy (XPS) is often used in interphase investigations of lithium ion batteries (LIBs), yet it is unclear if its results convey an accurate picture of the lithium loss in aged LIBs. Herein, electrochemical and surface analytical approaches were used to separately quantify the contribution of interphase growth to lithium loss in LIBs. For this, LIB pouch cells (NMC622||graphite, 5 Ah) were aged for 400 full cycles at 20 or 60°C. Electrodes were harvested post mortem and subsequent investigations in lithium metal battery cells showed notably higher reversible and irreversible lithium loss after 60°C than after 20°C cycling. While the interphases did not notably increase in thickness with aging, the surface area of both electrodes increased, leading to more electrolyte decomposition and larger lithium loss. Along with the surface area increase, more heterogeneous electrolyte decomposition product residues on the negative electrode surface and higher cathodeelectrolyte charge-transfer resistances were observed. In conclusion, the applied combination of XPS and nitrogen adsorption can quantify homogenously distributed electrolyte decomposition layers of thicknesses <10 nm, but not thick and heterogeneous decomposition product residues arising with 60°C cycling. For this, the need for an alternative quantification method is highlighted.
... Prior work has shown that cell degradation follows Arrhenius kinetics, therefore cell lifetime can be estimated as a function of temperature using the accelerated failure data obtained at higher temperatures. [4][5][6][7][8][9][10] Another important factor to consider for high-temperature operations is the selection of lithium salt. Increasingly, the salt lithium bis(trifluoromethanesulfonyl)imide (LiFSI) is used for its ability to improve cycle life by promoting the stability of the graphite (or silicon) negative electrode/electrolyte interface. ...
LiFePO4/graphite (LFP), Li[Ni0.5Mn0.3Co0.2]O2/graphite (NMC3.8V, balanced for 3.8 V cut-off), and Li[Ni0.83Mn0.06Co0.11]O2/graphite (Ni83, balanced for 4.06 V cut-off) cells were tested at 85°C. Three strategies were used to improve cell lifetime for all positive electrode materials at 85°C. First, low-voltage operation (< 4.0 V) was used to limit the parasitic reactions at the positive electrode. Second, LiFSI (lithium bis(trifluoromethanesulfonyl)imide) was used as the electrolyte salt for its superior thermal stability over LiPF6 (lithium hexafluorophosphate). The low-voltage operation avoids the aluminum corrosion seen at higher voltages with LiFSI. NMC3.8V cells were operated at 6C charge and 6C discharge without issue for 2500 cycles and then moved to room temperature where normal operation was obtained. Finally, dimethyl-2,5-dioxahexane carboxylate (DMOHC) was used as a sole electrolyte solvent or mixed with dimethyl carbonate. μ-XRF data showed no detectable levels of transition metal deposition on the negative electrode of Ni83 and LFP cells, and DMOHC cells showed less gassing after testing compared to EC-based electrolytes. We found incredible capacity retention and cycle life for Ni83 and NMC3.8V cells using DMOHC and LiFSI at 70°C and at 85°C in tests that ran for more than 6 and 3 months (and are still running), respectively.
... At temperatures typically below 25 °C, Li plating becomes favorable, also leading to strong capacity fading and lowered battery safety [14]. The exact optimum temperature of a Li-ion battery depends on multiple factors, such as cell chemistry and cell type (i.e., cylindrical cell, pouch cell, or prismatic cell), electrode thickness, and cycling rate [18,23]. While slight deviations from the optimum temperature are tolerable, more significant variations must be prevented [24]. ...
A constant and homogenous temperature control of Li-ion batteries is essential for a good performance, a safe operation, and a low aging rate. Especially when operating a battery with high loads in dense battery systems, a cooling system is required to keep the cell in a controlled temperature range. Therefore, an existing battery module is set up with a water-based liquid cooling system with aluminum cooling plates. A finite-element simulation is used to optimize the design and arrangement of the cooling plates regarding power consumption, cooling efficiency, and temperature homogeneity. The heat generation of an operating Li-ion battery is described by the lumped battery model, which is integrated into COMSOL Multiphysics. As the results show, a small set of non-destructively determined parameters of the lumped battery model is sufficient to estimate heat generation. The simulated temperature distribution within the battery pack confirmed adequate cooling and good temperature homogeneity as measured by an integrated temperature sensor array. Furthermore, the simulation reveals sufficient cooling of the batteries by using only one cooling plate per two pouch cells while continuously discharging at up to 3 C.
