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Limited Accessibility to Surface Area Generated by Thermal Pretreatment of Electrodes Reduces Its Impact on Redox Flow Battery Performance
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Predicting Li-ion Battery Performance for Impurity-doped NMC Cathodes Using Deep Learning
With the electric vehicle (EV) market expansion and the energy sector's shift towards electrification, the demand for battery metals, including lithium (Li), cobalt (Co), and nickel (Ni), is set to surpass supply. A critical knowledge gap exists in the purity standards for battery precursors and the impact of impurities on battery performance. Addressing this, our study employs a Deep Machine Learning (DL) based multi-objective optimization approach to interpret the relationship between metal impurities in domestic battery resources and their effects on battery performance. We analyze experimental data from Li-ion batteries with NMC (Nickel-Manganese-Cobalt oxide) cathodes over 1000 cycles, representing approximately ~6-8 months of operation, to establish a baseline of performance without impurities. Leveraging this data, we develop a Physics-Informed Deep Learning (PIDL) framework to extend our findings to cases that include metal impurities (e.g., Fe, Cu, Al) ranging from (0.001 - 0.01) %, respectively. By incorporating physics-based features, our PIDL model can accurately estimate the performance of NMC cathodes doped with various metal impurities to provide rapid design decisions. This research paves the way for informed decisions in Li-ion battery material design and optimization, ensuring the sustainable growth of the EV market and the broader energy sector.
Battery Performance and Cost Model (BatPaC) Version 6.0
SF-26-016 The Battery Performance and Cost model (BatPaC) is a calculation method based on Microsoft Excel spreadsheets that has been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEVs) and pure electrics. BatPaC was first developed in 2007, was subsequently peer reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. BatPaC has been updated and re-released multiple times since its original public release in 2011. This current version is BatPaC 6.0, which contains additional functionality needed to handle advances in automotive batteries, like the use of lithium metal and silicon anodes and the need to accommodate cell expansion and apply high levels of pressure.
Impact of Module Configuration on Lithium-Ion Battery Performance and Degradation: Part I. Energy Throughput, Voltage Spread, and Current Distribution
Batteries are commonly connected in series and parallel to create modules that fulfill the power and energy requirements of specific applications. However, conclusions about battery performance and degradation under different conditions, as well as predictive models, are often derived from single cell cycling results. In this study, we evaluate the performance of six different series-parallel configurations of commercial lithium nickel manganese cobalt cells over hundreds of cycles. Each cell within the modules was individually instrumented for voltage, current, and temperature monitoring. We quantified the impact of module configuration on overall energy throughput, the voltage spread among series-connected cells, and the current heterogeneity in parallel-connected cells. This module cycling study, one of the broadest reported to date, supports systematic evaluation of the performance trade-offs, pack penalty, and safety implications of different module configurations.
Battery Performance and Cost Modeling for Electric-Drive Vehicles (A Manual for BatPaC v5.0)
This manual details the fifth version of the Battery Performance and Cost (BatPaC v5.0) model developed at Argonne National Laboratory for lithium-ion battery packs used in transportation (file “BatPaC 5.0 2022-07-22.xlsm”). BatPaC is a publicly available model that performs a bottom-up lithium-ion battery design and cost calculation. The model designs the battery for a specified power, energy, and vehicle type (i.e., hybrid, plug-in hybrid, or full-electric). The cost of the designed battery is calculated by accounting for every step in the lithium-ion battery manufacturing process. The original model and manual were publicly peer-reviewed by battery experts assembled by the U.S. Environmental Protection Agency. This revised model and manual include changes made in response to comments received from users and the observed trajectory of the industry.
Impact of cycling conditions on lithium-ion battery performance for electric vertical takeoff and landing applications
The development of better electrochemical energy storage systems has sparked significant interest in using Li-ion batteries for electric vertical takeoff and landing (eVTOL) applications. To ensure the optimal performance and safety of onboard batteries, their behavior under different charging/discharging protocols and environmental conditions must be understood. Here, this paper presents a comprehensive evaluation of commercial Li-ion batteries for eVTOL applications, focusing on their responses to varying charging/discharging strategies and mechanical vibrations experienced during flight. Through controlled experiments, the effects of rapid cycling on battery performance were investigated, including effects on lifespan, capacity, and internal resistance. Additionally, the impact of mechanical vibrations on battery behavior was assessed to identify potential challenges for onboard batteries. The results of this study revealed intriguing insights into the interplay between temperature, vibration, and battery performance. This work contributes to the broader adoption of electric aerial transportation, promising a greener and safer future for urban mobility.
Battery Performance, Thermal, and Life Modeling for Southern California Edison (Cooperative Research and Development Final Report)
The objective of this project is to develop battery performance models, thermal models and life models for SCE to use in simulation and analysis of grid services involving energy storage. NREL will fit the models to three separate chemistries using test data provided by SCE. The dataset for each chemistry will include electrical, thermal and aging response to different temperatures and cycling conditions, measured under a variety of cell-level and module-level experiments described in SCE test protocol documents. Once tuned to a test dataset, the battery system model software will provide predictions of battery energy and power loss, and thus cycle and calendar life, for any energy storage grid service power profile of interest to SCE. The automated battery life modeling and simulation tool can also be applied to any other grid battery systems as long as necessary input data is available to accelerate battery model development and battery lifetime analysis.
Development of electronically passivating surfaces to enhance battery performance
For many decades, lithium metal batteries (LMBs) have been studied for their high energy density, though they suffer from high reactivity, which safety concerns such as explosions and fires. These events can occur as a result of degradation due to redox reactions and lithium morphology that evolve during cycling. Mostly, the failure is caused by the electrolyte degradation leading to the creation of a solid electrolyte interphase (SEI), which tends to induce electron leakage, lithium dendrite growth, and capacity decay. SEI formation is practically inevitable. This work investigated the minimization of degradation events by modifying different battery configurations. Modifications were made by exchanging electrolytes, electrode surfaces, and even annexing innovative components. Our goal is to achieve a configuration where Li-ion transfer is facilitated, and electron transfer is limited which would enable a longer lithium metal battery life cycle. When performing these modifications and testing their electrochemical performance, a current reduction was observed when the electrodes were spontaneously coated with preformed SEI. Herein several classes of artificial SEI layers are explored for their ability to form passivating layers through use of a redox active molecule.
Temperature-dependent Battery Performance of a Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT Cathode and In-situ Heat Generation on Cycling
Excellent structural stability, high operating voltage, and high capacity have made Na 3 V 2 (PO 4 ) 2 F 3 a promising cathode material for sodium-ion batteries. However, high-temperature battery performances and heat generation measurements have not been systematically reported yet. Carbon-coated Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT (multi-walled carbon nanotube) samples are fabricated by a hydrothermal-assisted sol-gel method and the electrochemical performances are evaluated at three different temperatures (25, 45, and 55 °C). Furthermore, the well-crystallized Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples exhibit good cycling stability at both low and high temperatures; they deliver an initial discharge capacity of 120–125 mAhg -1 at a 1 C rate with a retention of 53 % capacity after 1,400 cycles with 99 % columbic efficiency. The half-cell delivers a capacity of 100 mAhg -1 even at a high rate of 10 C at room temperature. Furthermore, the Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples show good long-term durability; the capacity loss is an average of 0.05 % per cycle at a 1 C rate at 55 °C. Additionally, ionic diffusivity and charge transfer resistance are evaluated as functions of state of charge, and they explain the high electrochemical performance of the Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples. In-situ heat generation measurements reveal reversible results upon cycling owing to the high structural stability of the material. Excellent electrochemical performances are also demonstrated in the full-cell configuration with hard carbon as well as antimony Sb/C anodes.
Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning
A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.
High Throughput Laser Processing for Enhanced Battery Performance and Manufacturing
For widespread adoption of electric vehicles, lithium-ion batteries (LiBs) need to achieve energy densities of >275 Wh/kg, cell costs less than $100/kWh, and charge to more than 80% capacity within 15 minutes. Recently, it has been shown that introducing a secondary pore network in thick battery electrodes enables higher extracted capacity without a severe reduction in charge/discharge rate performance. Secondary pore networks consisting of ordered arrays of microstructures reduce tortuosity of Li+-ion transport deep into the thick electrodes, diminishing detrimental concentration gradients within the cell. Ultrafast (femtosecond)-pulsed laser ablation is a promising method to introduce such micro pores or channels in thick battery electrodes as it allows for precise control of pattern geometries, results in minimal damage to the electrode, and can be introduced into existing roll-to-roll electrode manufacturing lines. We have explored laser patterning to create secondary pore networks in a variety of battery electrode materials for both anode and cathode applications and characterized the laser-material interaction parameters. In addition, advanced materials characterization techniques (SEM-EDS, XRD) were used to explore whether ultrafast laser ablation affects the remaining electrode materials' morphology and structure. Furthermore, we have translated lab-scale laser ablation to high-throughput roll-to-roll processing at industrially relevant scales. Finally, we worked with Argonne National Laboratory, developers of the battery cost-analysis package BatPac, to estimate the additional manufacturing costs (CapEx and OpEx) for incorporation of laser-ablation processing to current battery electrode manufacturing. We estimate that ultrafast laser processing will add only -$1kWh, which for considerable improvements in cell performance, creates a compelling case for industry adoption.
Impact of the fluorination degree of ether-based electrolyte solvents on Li-metal battery performance
Electrolytes using fluorinated solvents have proven effective in improving the cycling life of Li-metal batteries, by forming a robust solid–electrolyte interphase through decomposition of anion and fluorinated solvent molecules. Herein, we modulated the fluorination degree of ether-based electrolyte solvents to investigate their performance in Li-metal batteries. Here, we tuned the fluorination degree by installing a monofluoro substituent on one ethoxy group of 1,2-diethoxyethane (DEE) and varying the fluorination degree on the other one, providing three fluorinated DEE solvent molecules (i.e., F1F0, F1F1 and F1F2) with a relatively low fluorination degree. All three electrolytes showed improved solvation strength and ionic conductivities compared with previous highly fluorinated DEE electrolytes while retaining good oxidative stability. A full cell test using the Li-metal anode and nickel-rich cathode revealed that a higher degree of fluorination is beneficial to the cycling performance, and the cycling stability follows F1F0 < F1F1 < F1F2. Specifically, F1F0 exhibited poor cycling stability due to its instability against both the anode and cathode. While F1F1 and F1F2 both showed good stability against the Li metal anode, their relative long-term oxidative stability was responsible for the distinct performance, in which the cycle numbers at 80% capacity retention for F1F1 and F1F2 were ~20 and ~80, respectively. Finally, we demonstrated that F1F2 was able to achieve 90 cycles before reaching 80% capacity retention in practical lithium iron phosphate (LFP) pouch cells. This work shows the importance of modulating the fluorination degree of electrolyte solvents, and this approach is suitable for various cathode materials.
Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework
Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.
Enabling High-Temperature and High-Voltage Lithium-Ion Battery Performance through a Novel Cathode Surface-Targeted Additive
We report lithium-ion batteries (LIBs) are being used in locations and applications never imagined when they were first conceived. To enable this broad range of applications, it has become necessary for LIBs to be stable to an ever broader range of conditions, including temperature and energy. Unfortunately, while negative electrodes have received a great deal of focus in electrolyte development, stabilization of positive electrodes remains an elusive target. Here, we report a novel additive that shows the ability to protect positive electrodes against elevated temperatures and voltages. This additive can be used in small quantities, and its targeted behavior allows it to remain functional in complex electrolyte packages. This can prove an effective approach to targeting specific aspects of cell performance.
Surface coating by mechanofusion modulates bulk charging pathways and battery performance of Ni-rich layered cathodes
Ni-rich layered oxides as high-capacity battery cathodes suffer from degradation at high voltages. Here, we utilize a dry surface modification method, mechanofusion (MF), to achieve enhanced battery stability. The simplicity, high yield, and flexibility make it cost-effective and highly attractive for processing at the industrial scale. The underlying mechanisms responsible for performance improvement are unveiled by a systematic study combining multiple probes, e.g., 3D nano-tomography, spectroscopic imaging, in situ synchrotron diffraction, and finite element analysis (FEA). MF affects the bulk crystallography by introducing partially disordered structure, microstrain, and local lattice variation. Furthermore, the crack initiation and propagation pattern during delithiation are regulated and the overall mechanical fracture is reduced after such surface coating. We validate that MF can alter the bulk charging pathways. Such a synergic effect between surface modification and bulk charge distribution is fundamentally important for designing next-generation battery cathode materials.
Comprehensive evaluation of commercially scalable atomic-layer-deposited alumina coating impact on full cell battery performance across varied test conditions
Atomic Layer Deposition (ALD) has emerged as a strategic enhancement method for lithium-ion battery (LIB) materials offering potential benefits and durability benefits for industrial battery production. However, the translation from laboratory achievements to commercial-scale applications has been limited. Here, this study aims to bridge this gap by comprehensively evaluating the effects of commercially scalable Al 2 O 3 ALD coatings using full pouch cell performance as a means to assess the ALD impact. We utilized large-scale slot-die coating techniques to ensure consistent electrode quality and tested four configurations of pouch cells to analyze the individual effects of ALD coating on anode and cathode electroactive materials. Our extensive testing matrix included long-term cycling, fast discharge, fast charge, leakage current, and high voltage tests. While at lower C-rates (<~1C), the influence of Al 2 O 3 coatings on cell performance is not significant. Fast charging conditions reveal that the anode ALD coating significantly enhances performance via a passivating effect, while on the cathode, it is detrimental, potentially due to increased resistance of the thin interfacial layer formed during the ALD processing. Leakage current and high-voltage tests show that the application of ALD coatings on either anode or cathode effectively minimizes side reactions at the electrode-electrolyte interface. Additionally, ALD coatings significantly mitigate concentrated and localized lithium plating on the anodes. These insights provide a valuable understanding of the potential of ALD technologies in LIB manufacturing to tailor cell performance, paving the way for safer, more efficient, and cost-effective battery solutions.
Low-Rate Cycling Regeneration Sustains NMC811–Graphite Battery Performance through Extreme Ultrafast Charging
Urban air mobility (UAM) systems, such as electric vertical takeoff and landing (eVTOL) aircraft, require batteries capable of extreme ultrafast charging to support high-frequency operations. Here, in this work, we evaluate the electrochemical stability of NMC811–graphite full cells using a complex mixed-modal protocol designed to simulate the aggressive turnaround demands of UAM missions. We demonstrate that incorporating a periodic low-rate (0.3C) “regeneration” cycle enables cells to maintain 96% capacity retention over 900 cycles, despite repeated exposure to ultrafast pulses. To validate the efficacy of this regeneration mechanism, we performed simplified control experiments comparing continuous 10C charging against a 10C–0.3C interleaved protocol. Advanced characterization reveals that while mechanical particle fracture is pervasive at these rates, the regeneration cycles decelerate the electrochemical degradation by resolving spatially heterogeneous Ni oxidation states and relaxing lithium-ion concentration gradients. These findings provide a vital mechanistic framework for battery operation, proving that restorative charging can effectively delay and mitigate failure in demanding UAM applications.