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In order to meet the applications for Space Shuttle in the future, Samsung 18650 cylindrical Li-ion cells with two different capacities have been evaluated. The capacities are 1800 mAh, and 2000 mAh. The studies focused on the performance and safety tests of the cells.
This disclosure provides systems, methods, and apparatus related to Li-ion batteries. In one aspect an electrolyte structure for use in a battery comprises an electrolyte and an interconnected boron nitride structure disposed in the electrolyte.
This disclosure provides systems, methods, and apparatus related to Li-ion batteries. In one aspect an electrolyte structure for use in a battery comprises an electrolyte and an interconnected boron nitride structure disposed in the electrolyte.
As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.
Non-flammable and high-temperature stable ionic liquid (IL)-based electrolytes could eliminate catastrophic battery failures and improve battery safety, but their poor electrochemical interaction with the LiNi x Mn y Co z O 2 (NMC) family of cathodes is a long-standing problem due to severe parasitic reactions at high temperature. Understanding surface and bulk structural mechanisms of NMC-type cathodes at elevated operational temperature is of paramount importance to facilitate stable electrochemical performance. Here, we report a non-flammable phosphonium IL-based cell chemistry that offers stable electrode-electrolyte interfaces, leading to electrochemical performance up to 125°C and high-temperature safety. We combine electrochemistry with multimodal X-ray spectroscopy methods to understand interfaces at elevated temperature (100°C). This nanoscale understating enables a proof-of-concept high-temperature cylindrical cell (14500), and the design achieves an average Coulombic efficiency of ≈99.5% up to 300 cycles at 100°C. The results ascertain the significance of depth-dependent degradation at the interface, guiding room-temperature Li-ion technology toward extreme-temperature applications.
The overall approach will focus on the development and optimization of synthesis methods and precursors for multifunctional Li-ion conducting interfacial materials, protective layer composition, and protective layer fabrication methods. This will be accompanied by property measurement and characterization (conductivity, mechanical properties, etc.) for the protective layer on Li metal, along with cell testing.
The replacement of lithium metal by lithium intercalated graphite was crucial for developing safe lithium-ion batteries. Superoxide-based potassium–oxygen batteries represent an exciting metal–oxygen battery with the highest energy efficiencies. However, using potassium metal is a more serious safety threat than lithium. Herein, we explore the possibility of graphite-intercalation anodes for potassium-ion oxygen batteries (PIOBs) with enhanced safety. This work demonstrates for the first time that establishing an artificial potassium salt-rich solid electrolyte interphase (SEI) enables a reversible graphite-intercalation anode (249.6 mA h g -1 after 600 cycles) in a potassium bis(trifluoromethanesulfonyl)imide (KTFSI)-based localized high-concentration electrolyte. Such an electrolyte is stable with the superoxide cathode. The PIOB delivers energy efficiencies above 90% at a depth of discharge (DOD) of 25% for 80 cycles. A three-electrode measurement shows that its overpotential mainly comes from the anode. The lifespan is limited to the gradual degradation of the artificial SEI caused by oxygen crossover. This work represents a step towards achieving holistic anode–electrolyte–cathode compatibility in a PIOB and its realistic evaluation under a controlled DOD.
Metastable orthorhombic niobium pentoxide (T-Nb 2 O 5 ) is a promising anode to fulfill the requirements for high-rate Li-ion batteries (LIBs). Stoichiometric T-Nb 2 O 5 is plagued by low electric conductivity and particle pulverization after repeated charge/discharge processes. In this work, oxygen vacancies are implanted into T-Nb 2 O 5 particles via acid immersion of Nb 2 O 5 ·nH 2 O with the formation of Lewis acid sites. The multiple characterizations and simulations reveal the lengthening of Nb–O bonds, and the transformation from NbO 7 pentagonal bipyramids and NbO 6 tetragonal bipyramids in T-Nb 2 O 5–x . The enrichment of oxygen vacancies endows T-Nb 2 O 5–x with higher electric conductivity, better electrochemical kinetics, larger pseudocapacitive contribution. O-doped graphitic C 3 N 4 is creatively proposed as a trace oxygen pump to repair excessive oxygen vacancies, and it also serves as a sacrifice template for Nb 2 O 5–x growth to construct a porous and monolithic electrode network. Defect-modulated Nb 2 O 5–x displays extraordinary cycling stability (164 mAh g –1 at 5 C after 1100 cycles), high capacity retention (104 mAh g –1 ) at an ultrahigh rate (25 C), and large areal capacity (0.74 mAh cm –2 ) under high mass loading (4 mg cm –2 ). The practical prospect is proved by Nb 2 O 5–x /LiNi 0.8 Co 0.1 Mn 0.1 O 2 full cells with high average platform (2.12 V) and high specific capacity (229 mAh g –1 ). Finally, the oxygen-defect modulation strategy on oxide anodes provides an alternative solution to fast-charging and durable LIBs.
Characterization of the rate and quality of ultrafast-laser ablation of Li-ion battery (LIB) electrode materials is presented for a collection of common and next-generation electrodes. Laser ablated micro-structures on the surface of LIB electrodes have been shown to provide dramatic enhancement of high-rate capability and electrode wetting. However, industrial adoption is hampered by a lack of data enabling informed choice of laser parameters and predicting process throughput. Here this work bridges this gap by providing characterization of the ablation process at more laser parameters (laser fluence and number of pulses used) than are currently available in the literature. Further, we expand on previous graphite and lithium iron phosphate (LFP) ablation work by extending ablation characterization to new LIB materials, providing high data resolution and adopting new characterization metrics which are relevant for industrial application of this technology. Ablated pores are characterized by their ablated depth, volume, and how the depth and volume ablation rate changes as a function of pore depth. Finally, we provide a detailed characterization of the morphology of laser ablated micro-structures which informs how material and laser parameters affect the quality of laser-processed electrodes.
We discuss the characteristic factors that determine the electrochemical potentials in a metal-organic framework used as cathode for Li-ion batteries via density functional theory-based simulations. Our focus is on MIL-101(Fe) cathode material. Our study gives insight into the role of local atomic environment and structural deformations in generating electrochemical potential.
The high specific capacities of Ni-rich transition metal oxides have garnered immense interest for improving the energy density of Li-ion batteries (LIBs). Despite the potential of these materials, Ni-rich cathodes suffer from interfacial instabilities that lead to crystallographic rearrangement of the active material surface as well as the formation of a cathode electrolyte interphase (CEI) layer on the composite during electrochemical cycling. While changes in crystallographic structure can be detected with diffraction-based methods, probing the chemistry of the disordered, heterogeneous CEI layer is challenging. In this work, we use a combination of ex situ solid-state nuclear magnetic resonance (SSNMR) spectroscopy and X-ray photoemission electron microscopy (XPEEM) to provide chemical and spatial information, on the nanometer length scale, on the CEI deposited on LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) composite cathode films. XPEEM elemental maps offer insight into the lateral arrangement of the electrolyte decomposition products that comprise the CEI and paramagnetic interactions (assessed with electron paramagnetic resonance (EPR) and relaxation measurements) in 13 C SSNMR provide information on the radial arrangement of the CEI from the NMC811 particles outward. Using this approach, here we find that LiF, Li 2 CO 3 , and carboxy-containing structures are directly appended to NMC811 active particles, whereas soluble species detected during in situ 1 H and 19 F solution NMR experiments (e.g., alkyl carbonates, HF, and vinyl compounds) are randomly deposited on the composite surface. We show that the combined approach of ex situ SSNMR and XPEEM, in conjunction with in situ solution NMR, allows spatially-resolved, molecular-level characterization of paramagnetic surfaces and new insights into electrolyte oxidation mechanisms in porous electrode films.
An investigation is presented into the application of active life balancing battery management systems to recondition inhomogeneous Li-ion battery packs and achieve homogeneity in capacity for second life. This paper proposes a life balancing objective map, which controls the state of charge references for each cell in a battery pack based on its capacity relative to other cells. The impacts of life balancing objective maps on the reconditioning time and overall pack capacity fade are considered. Moreover, the proposed approach accounts for various load and environmental effects, including depth-of-discharge, battery pack temperature, number of cycles per day, and cells’ resting time at various states of charge. The study results in a battery pack with convergent capacities achieved in minimal time, while curtailing overall capacity fade. Simulation results for a battery pack with sixteen series connected 75 Ah Kokam NMC cells with 3.6% initial capacity imbalance are presented, indicating a reconditioning time of 1.34 months with a final capacity imbalance of 0.1% and an overall capacity fade of 0.005%.
Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.
This paper describes the development of a thick-film microcathode for use in Li-ion microbatteries in order to provide increased power and energy per area. These cathodes take advantage of a composite porous electrode structure, utilizing carbon nanotubes (CNT) as the conductive filler. The use of carbon nanotubes was found to significantly reduce the measured resistance of the electrodes, increase active material accessibility, and improve electrode performance. In particular, the cycling and power performance of the thick-film cathodes was significantly improved, and the need for compression was eliminated. Cathode thickness and CNT content were optimized to maximize capacity and power performance. Power capability of >50 mW/sq cm (17 mA/sq cm) with discharge capacity of >0.17 mAh/sq cm was demonstrated. The feasibility of fabricating thick-film microcathodes capable of providing the power and capacity needed for use in autonomous microsensor systems was also demonstrated.
In order to tackle and solve the prediction problem of the lifetime of Li-ion batteries, it is essential to have awareness of the current state and health of the battery pack. To be able to accurately predict the future state of any system, one must possess knowledge of its current and future operations. Using derived models of the current and future system behavior, a model-based prognostics approach can be implemented as a solution to the prediction problem. As more and more autonomous electric vehicles progressively emerge in our daily life, a very critical challenge lies in accurate prediction of remaining useful life of the systems/subsystems. Batteries, power electronics conditioning systems, and motors are integrated to form the powertrain in electric vehicles; one of the most critical systems. In the case of electric aircrafts, computing remaining flying time is critical for safety, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastropheThis presentation covers a physics-based modeling approach implemented for case studies in capacitor and battery prognostics which are an integral part of an electrical powertrain system. The general approach of model-based prognostics will be examined as a potential solution for safety critical problems related to battery state of charge and state of health.
The self discharge test at full state of charge, may not be a good one to detect subtle defects since the li-ion chemistry has the highest self discharge at full state of charge. One should characterize self discharge versus storage time for each cell manufacturer/design to differentiate between normal self discharge and that due to a subtle manufacturing defect. The various soft short test methods indicate that if this test is carried out at full discharge (0% SOC) with all capacity removed (by lowering the current load in a stepwise manner to the same end of discharge voltage), then the cells need to be placed in storage for more than 72 hours to get a good analysis on the presence of subtle defects since it takes more than 72 hours to achieve voltage stabilization. If the cells are to be charged up even to a small percentage (ex. 1%), 72 hours are sufficient to determine issues. However, the pass/fail criteria should be based on a valid OCV decline. Less than 10 mV voltage decline is not a good method to detect subtle defects. As mentioned in the first bullet, self discharge is a competing reaction when a charge is introduced and hence a characterization of the self discharge versus storage time is required to fully correlate voltage decline to a failure due to a subtle defect. Soft short test method cannot be relied on for defect detection because cells with and without voltage decline seemed to have similar defects and characteristics. Screening methods such as internal resistance and capacity as well as a 3-sigma range for OCV, mass and dimensions should be used to screen out outliers. A very critical aspect in the understanding of subtle defects is to carry out destructive analysis of cells from every lot to confirm the quality of production and screen all cells and batteries in a stringent manner to have a high quality set of flight cells. Self Discharge Test: Fully charged cells shall be placed in Open circuit stand for 72 hours (OCV measurement twice a day); continue for total of 14 days with 1 reading per day 2. Soft Short Test 1: Fully charge; cells discharged to manufacturer's end of disch. Voltage (EODV) cutoff at C/5 rate; stand for 30 minutes; discharge with C/500 to the same EODV. stand for another 30 minutes; discharge the cells again using C/1000 current to the same EODV. OCV measurements twice a day for 72 hours and then for total of 14 days (data collection same as in 1.) 3. Soft Short Test 2: Fully charge; cells discharged to the manuf. EODV with a C/13 constant current; provide a 10 hour rest, discharge again to the same EODV with a current of C/250, provide a 10 hour rest, discharge again using a C/250 rate, provide a 24 hour rest, charge using C/250 to 3.15 V (for ~12 hours). OCV measurements twice a day for at 72 hours. (data collection same as in 1.) 4. Soft Short Test 3: Fully charge; cells shall be discharged using C/10 current to manuf. EODV. Allow the cell to remain at Open circuit for 10 seconds. Discharge the cell at C/20 rate to the same EODV, hold open circuit for 24 hours. Discharge the cells at C/200 rate to the same end of voltage cutoff and hold open circuit for 24 hours. Discharge the cells one more time at C/200 rate to the same EODV and hold open circuit for 36 hours. Charge at C/200 rate to 3.15 V and hold for 3 days. Record OCV during the open circuit stand periods every 12 hours and at the beginning and end of the 3 day hold (include the 12 hour OCV recording during this time also). Capacity Cycling: Cells with declining voltages - one cell from each manufacturer chosen for cycling Destructive Physical Analysis (DPA): Cells with and without decline chosen from each lot for DPA.
As the demand for high-performance and reliable energy storage devices continues to rise, identifying new anode materials is crucial for advancing Li-ion battery (LIB) technology. Inspired by recent experimental breakthroughs in synthesizing two-dimensional transition metal carbo-chalcogenides (2D-TMCCs), density functional theory calculations are performed to systematically explore their sulfide variants (TM 2 S 2 C) spanning all 3d transition metals in three possible phases. Through comprehensive evaluations of thermodynamic, dynamic, mechanical, and thermal stabilities, seven stable 2D-TMCC candidates are identified, four of which exhibit superior battery performance. Notably, V-based 2D-TMCCs across all three phases deliver moderate open-circuit voltages (OCV), efficient Li diffusion, and substantial capacities, making them promising candidates for industrial applications without requiring specific phase controls. A Cr-based 2D-TMCC (with sulfur atoms above carbon atoms) offers the highest capacity of 515.40 mAh g −1 , the lowest Li diffusion barrier, and an optimal OCV, highlighting its appealing potential as an anode material for LIBs. Furthermore, significant Li–Li spacing and pronounced electron delocalization in these four 2D-TMCCs suggest a reduced risk of dendrite formation. This work expands the 2D-TMCC family and identifies up-and-coming candidates for next-generation LIB anodes.
Herein, we evaluate the effect of covalently attached molecular coating hydrophobicity on the surface of the silicon nanoparticle (Si NP) active anode material for Li-ion batteries. The experiments are a means to identify the interfacial properties that help minimize electrochemical side reactions during cycling. Preformed coatings on the Si NP surfaces prior to electrode fabrication mimic the ionically conducting and electronically insulating properties of the solid electrolyte interphase (SEI). Hydrophilic oligomers such as polyethylene oxide (PEO) and other related structures are commonly identified as Li+-conducting components of the SEI. Here, we study the effect of such hydrophilic PEO versus hydrophobic alkyl molecular coatings on Si NP anode electrochemical performance. We also study the effect of the PEO oligomer length and the resulting effective thickness of the interfacial coating on the electrochemical performance. We find that PEO oligomers electrochemically isolate Si NPs when the PEO coating thickness approaches the electron tunneling distance of ~2.5 nm. Surprisingly, the thickness of the PEO-based coatings has a negligible effect on their ability to minimize electrochemical side reactions as measured by Coulombic efficiency. These results reveal how the interfacial coating on silicon anode materials should differ from an operando-formed SEI layer and discuss design strategies for an ideal interfacial active material coating based on these results.