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Poster presentation for DOE Vehicle Technologies Office Annual Merit Review (AMR) Meeting, June 2-5, 2025, Arlington, Virginia
Abstract End of life (EoL) lithium‐ion batteries (LIBs) are piling up at an intimidating rate, which is alarming for environmental health. With further expected rapid growth of LIB use, the magnitude of spent battery accumulation is also expected to grow. LiNi x Co y Mn z O 2 (NCM) cathode materials are a dominant chemistry in high energy LIBs, and make up a huge portion of this waste accumulation. Direct recycling is one of the most promising ways to turn this waste to wealth, but has been limited to lab‐scale, due to lack of robustness, namely the tedious pretreatment required that involves toxic organic solvents. Herein, a process that integrates the pretreatment and relithiation of the cathode black mass is demonstrated. Cathode material from EoL electric vehicle (EV) batteries is treated in a 100 g per batch operation and the regenerated cathode active material demonstrates 100% electrochemical performance recovery, with 91% yield rate, and shows promise for further scale up. This process has the advantages of integration, scalability, and universality, which clears the barricade for direct recycling to move from lab to industry scale with considerable profitability.
Lithium-Sulfur batteries are promising as the next-generation of low-cost, high-energy rechargeable batteries. However, the commercialization of this battery has been limited by poor rate capability and cycle life. To improve the performance of Li-S cells, we have investigated the role of two n-dopable conducting polymers, N2200 and N2200-OE, as binders for the sulfur cathode. The electron and ion transport properties of these polymer binders, along with their strong affinity for polysulfides, results in significantly enhanced performance compared to cells with the traditional insulating PVDF binder. The role of these conducting polymer binders in enhancing the performance of the cells is analyzed using impedance spectroscopy, shuttle current measurements, conductivity measurements, UV–vis spectroscopy, GIWAXS studies and post-cycling analysis of the anodes. The conducting polymer binders reduce the cell impedance by a factor of four leading to a marked improvement in rate capability. The shuttling of the polysulfides and the formation of insoluble sulfides at the anode is curtailed by the interaction of the polysulfides with the backbone of the polymer binders, leading to an impressive capacity retention of 82% after 500 cycles. These studies demonstrate the benefit of tailored polymer binders at the sulfur electrode in addressing the limitations of lithium-sulfur batteries.
A high-level overview of policy and regulatory considerations for the reuse and end-of-life management of solar photovoltaic equipment, such as modules, and large-format lithium-ion batteries used in mobile and stationary energy storage.
Utilizing the remaining capacity in retired lithium-ion (Li-ion) batteries from electric vehicles (EVs) for second-life applications has shown economic and environmental benefits. However, achieving homogeneity among the capacities of cells before their second life is critical to exploit the benefits. This article proposes a new active reconditioning approach with the potential to make short-term reconditioning of batteries before second life feasible. A control objective map determines each cell's state-of-charge (SOC) operating window based on its capacity relative to other cells. The SOC reference translates into distinct differential currents through the cells, wherein the higher-capacity cells undergo more frequent and deep charge and discharge cycles than their lower-capacity counterparts. The proposed solution achieves capacity homogeneity within the battery pack with low reconditioning time and minimal fade in the overall pack capacity. The feasibility of the reconditioning approach under varying load and environmental conditions is assessed through simulations, encompassing factors such as the number of cycles per day, depth-of-discharge, battery pack temperature, and cell resting time at different SOCs. Furthermore, the simulation model employs a battery pack with sixteen series-connected 75 Ah Kokam lithium nickel manganese cobalt oxide (NMC) cells with a 3.6% initial capacity imbalance. A reconditioning time of 1.3 months is achieved with a final capacity imbalance of 0.1% and an overall capacity fade of 0.005%, thereby confirming the viability of the reconditioning process. Moreover, experimental validation using eight retired battery cells from a Nissan Leaf demonstrates a substantial decrease in the capacity imbalance of cells from 9.4% to 2.15% within 78 days, effectively affirming the efficacy of the proposed reconditioning scheme.
This presentation describes work associated with the early prediction of life and failure modes using machine learning. Key areas which will be highlighted are the generation of synthetic data, the use of deep learning and other machine learning models and the use of machine learning to train life prediction models.
All-solid-state lithium metal batteries can address crucial challenges regarding insufficient battery cycling life and energy density. The demonstration of long-cycling dendrite-free all-solid-state lithium metal batteries requires precise tailoring of lithium-ion transport of solid-state electrolytes (SSEs). Here, in this work, a proof of concept is reported for precise tailoring of lithium-ion transport of a halide SSE, Li 3 InCl 6 , including intragranular (within grains) but also intergranular (between grains) lithium-ion transport. Lithium-ion migration tailoring mechanism in crystals is developed by unexpected enhanced Li, In, and Cl vacancy populations and lower energy barrier for hopping. The lithium-ion transport tailoring mechanism between the grains is determined by the elimination of voids between grains and the formation of unexpected supersonic conducting grain boundaries, boosting the lithium dendrite suppression ability of SSE. Due to boosted lithium-ion conduction and dendrite-suppression ability, the all-solid-state lithium metal batteries coupled with Ni-rich LiNi 0.83 Co 0.12 Mn 0.05 O 2 cathodes and lithium metal anodes demonstrate breakthroughs in electrochemical performance by achieving extremely long cycling life at a high current density of 0.5 C (2000 cycles, 93.7% capacity retention). This concept of precise tailoring of lithium-ion transport provides a cost, time, and energy efficient solution to conquer the remaining challenges in all-solid-state lithium-metal batteries for fast developing electric vehicle markets.
Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.
A daunting challenge in the design of lithium ion batteries (LiBs) is enabling 10-min extreme fast charging (XFC) while achieving appreciable charge acceptance and cycle life. This desirable outcome requires both a comprehensive understanding of LiB operation and aging behavior at different length scales and careful optimization. Lithium plating has been a critical bottleneck because, at XFC rates, it consumes cyclable lithium causing distinct aging and safety concerns even in moderate-loading LiBs. Here, we propose combining multiple solutions, including materials-to-electrode design-to-charging protocols, that are intended to overcome limitations in lithium-ion transport in the electrolyte phase, thus enabling 10-min XFC in LiBs. Some implemented strategies include cathode chemistry, optimized carbon binder domain in the cathode, dual layer anode design, improved separator and advanced electrolyte. Further, innovative charging protocols in moderately loading (~3 mAh/cm 2 anode/2.7 mAh/cm 2 cathode) single-layer pouch cells are proposed, together with demonstrated 10-min XFC with higher charge acceptance between 87.3 and 92.1% (or 2–2.1 mAh/cm 2 ) for 600 cycles without lithium plating. This methodical study with well-defined cells shows promise in combining multiple solution strategies to enable 10-min XFC, charting a pathway to achieve XFC in higher-loading energy-optimized LiBs.
Lithium-ion batteries (LIBs) have revolutionized portable electronics and electric vehicles (EVs), but the growing accumulation of end-of-life (EOL) batteries poses environmental challenges. Recycling high-value cathodes from EOL LIBs can minimize waste and reduce the need for mining critical minerals. Here, this study focuses on the direct recycling of Ni-rich cathodes, particularly lithium-manganese-cobalt-oxide (NMC) 622 in a “reciprocal ternary molten salts (RTMS)" system. The ionothermal relithiation in the RTMS system successfully restores the layered structure, lithium content, and electrochemical performance of the NMC 622 cathode, comparable to the pristine material. The cost analysis reveals that cathode regeneration through ionothermal relithiation is more economical than virgin production or conventional recycling methods.
Due to manufacturing variation, battery cells often possess heterogeneous characteristics, leading to battery state-of-charge variation in real-time. Since the lowest cell state-of-charge determines the useful life of battery pack, such variation can negatively impact the battery performance and electric vehicles range. Existing research has been focused on control design to mitigate cell imbalance. However, it is yet unclear how much impacts the cell imbalance can have on electric vehicle range. This paper closes this knowledge gap by using a simulation environment consisting of real-world driving speed data, vehicle longitudinal control, propulsion and vehicle dynamics, and cell level battery modeling. In particular, each battery cell is modeled as an equivalent circuit model, and variations among cell parameters are introduced to assess their impact on electric vehicles range and to identify the most influential parameter variations. Simulation results and analysis can be used to assist balancing control design and to benchmark control performance.
Lithium-ion batteries are used in a wide variety of applications. To meet the power and energy demands of these applications battery packs are composed of hundreds to thousands of cells. The electrical and thermal interactions between cells introduce additional complexity in the pack dynamics. To capture these effects, a battery pack model composed of 192 cells based on a first-generation (2012) Nissan Leaf battery pack is developed in MATLAB/Simulink/Simscape. Here, with this model, we simulate the electrical dynamics (using a first-order equivalent-circuit model), the thermal dynamics (using a first-order lumped-parameter thermal model), and the aging dynamics (using a semi-empirical severity factor-based model) of every cell in the pack and we also create a pack thermal model that explicitly captures the heat exchange between the modules, and the cells contained within, during operation. The models are calibrated and validated, both at the cell and pack level, with experimental data. Two different case studies of this pack model are investigated. In the first case study, an initial, normally-distributed, cell-to-cell capacity variation is introduced and its effect on the pack voltage and module temperatures is studied. In the second case study, we deliberately insert cells with lower than nominal capacity into the pack and we investigate how this type of initial cell-to-cell capacity variation affects the pack’s ability to deliver energy over time. Finally, we also study how parallel-connected cells can reduce the effects of cell-to-cell variations at the expense of increased aging of the pack overall.
Rechargeable lithium (Li) metal batteries have attracted wide attentions as the next generation energy storage technologies. However, simultaneously achieving high cell-level energy density and long cycle life in realistic batteries is still a great challenge. Here we investigate the cell degradation mechanisms of Li||LiNi0.6Mn0.2Co0.2O2 pouch cells using different, but representative cell configurations to understand the fundamental linkage among Li thickness, electrolyte depletion and the structure evolution of solid electrolyte interphase (SEI) layers. Different cell failure modes were discovered when tuning the anode to cathode capacity (N/P) ratio in compatible electrolyte. With a thick-Li anode (N/P ratio = 2.5), initial stable cycling is obtained because of the abundant Li supply from the anode together with an artificially inflated high Coulombic Efficiency, followed by a premature sudden cell death appears due to the enrichment of “ineffective SEI” which does not participate in the electrochemical reactions but keep increasing cell impedance. The anode-free cell (N/P 0:1) displays a steady capacity decay because cathode Li loss dominates from the beginning to the end of cell cycle life. An optimized thin-Li (N/P 1:1) well balances the Li consumption rate with the impedance buildup by minimizing the growth of ineffective SEI layer, thus decelerates cell polarization increasing and extends cycling. Contrary to conventional wisdoms, long cycle life is observed by using ultra thin-Li (20 µm) in balanced cells. A prototype 350 Wh kg-1 pouch cell (2.0 Ah) achieves over 600 long stable cycles with 76% capacity retention without sudden cell death.
The demand for solar photovoltaics (PV) and lithium-ion batteries (LIB) is expected to continue in the U.S. as the call for solar is projected to quadruple by 2030 to meet renewable energy and decarbonization goals. The expected demand for PV and LIB brings supply chain concerns and a growing need for a circular economy for PV and LIB materials. Domestic reuse and recycling is one potential circular economy solution for PV and LIB. This presentation explores the current U.S. law and regulatory landscape for the reuse and recycling of PV and LIB materials, and how certain policy frameworks impact reuse and end-of-life management decisions for PV and LIB materials.
Create analytical framework to capture costs and benefits of the automated sorting into battery recycling including the development and deployment of various types of battery recycling technologies such as pyrometallurgical, hydrometallurgical, and direct recycling. Li Industries, Inc. is a Virginia startup company focused on reinventing how lithium-ion batteries (LIBs) are recycled. Li Industries is focused on developing direct LIB recycling and automated battery sorting technologies in order to reduce the environmental impact of the LIB lifecycle. This work is to be conducted in support of the American-Made Challenges Lithium-Ion Battery Recycling Prize. Li Industries and NREL will work together to understand how novel technologies, such as those being developed by Li Industries, can impact the development and economics of the battery recycling industry. This voucher is being used to evaluate the profitability of an automated sorting system developed by Li Industries and the potential effect this increased value could have on the domestic lithium-ion battery (LIB) recycling industries in the United States. NREL has developed the Lithium-Ion Battery Resources Assessment (LIBRA) system dynamics model to project the future viability of the US LIB manufacturing and recycling industries under varying technoeconomic conditions and battery adoption scenarios over the coming decades. Additional logic was added to LIBRA to analyze the role automated sorting of recycling feedstock could play in the buildout of the industry and the impacts it has on the recovery of end-of-life (EOL) battery materials. This report summarizes the outcomes of this modeling analysis in the US context through a series of sensitivity analyses run for a range of values of a given input dimension and compared across the unsorted or automated sorting cases for LIB recycling feedstock. For greater detail on the process and analysis, the researchers are publishing a forthcoming journal article titled Techno-Economic Impact of a Smart Battery Sorting System for Lithium-Ion Battery Recycling and Mineral Recovery in the United States by Weigl, et al. In the event the article is not accepted by any currently seeking publication in academic or industry journal, it may be published by NREL. CRADA benefit to DOE, Participant, and US Taxpayer: assists laboratory in achieving programmatic scope competencies, uses the laboratory's core competencies.
Single-crystal materials have played a unique role in the development of high-performance cathode materials for Li batteries due to their favorable chemomechanical stability. The molten salt synthesis method has become one of the most prominent techniques used to synthesize single-crystal layered and spinel materials. In this work, the molten salt synthesis method is used as a technique to tune both the morphology and Mn 3+ content of high-voltage LiNi 0.5 Mn 1.5 O 4 (LNMO) cathodes. The resulting materials are thoroughly characterized by a suite of analytical techniques, including synchrotron X-ray core-level spectroscopy, which are sensitive to the material properties on multiple length scales. Furthermore, the multidimensional characterization allows us to build a materials library according to the molten salt phase diagram as well as to establish the relationship among synthesis, material properties, and battery performance. The results of this work show that the Mn 3+ content is primarily dependent on the synthesis temperature and increases as the temperature is increased. Additionally, the particle morphology is mostly dependent on the composition of the molten salt flux, which can be tailored to obtain well-defined octahedrons enclosed by (111) facets, plates with predominant ($11\tilde{2}$) facets, irregularly shaped particles, or mixtures of these. The electrochemical measurements indicate that the Mn 3+ content has a larger contribution to the battery performance of LNMO than do morphological characteristics and that a significant amount of Mn 3+ could become detrimental to the battery performance. However, with similar Mn 3+ contents, morphology still plays a role in influencing the battery cycle life and rate performance. The insights of molten salt synthesis parameters on the formation of LNMO, with deconvolution of the roles of Mn 3+ and morphology, are crucial to continuing studies in the rational design of LNMO cathode materials for high-energy Li batteries.
Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years. Furthermore, both large parameter spaces and high sampling variability necessitate a large number of experiments. As such, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users. We combine two key elements to reduce the optimization cost: an early-prediction model, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.
Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.