Abstract Thickening electrodes is one effective approach to increase active material content for higher energy and low‐cost lithium‐ion batteries, but limits in charge transport and huge mechanical stress generation result in poor performance and eventual cell failure. This paper reports a new electrode fabrication process, referred to as µ‐casting, enabling ultrathick electrodes that address the trade‐off between specific capacity and areal/volumetric capacity. The proposed µ‐casting is based on a patterned blade, enabling facile fabrication of 3D electrode structures. The study reveals the governing properties of µ‐casted ultrathick electrodes and how this simultaneously improves battery energy/power performance. The process facilitates a short diffusion path structure that minimizes intercalation‐induced stress, improving energy density and cell stability. This work also investigates the issues with structural integrity, porosity, and paste rheology, and also analyzes mechanical properties due to external force. The µ‐casting enables an ultrathick electrode (≈280 µm) that more effectively utilizes NMC‐811 (LiNi0.8Mn0.1Co0.1O2) cathode and mesocarbon microbeads anode active materials compared to conventional thick electrodes, allowing high‐mass loading (35.7 mg cm−2), 40% higher specific capacity, and 30% higher areal capacity after 200 cycles, high C‐rate performance, and longer cycle life.
more »
« less
Electric Vehicle Battery Simulation: How Electrode Porosity and Thickness Impact Cost and Performance
Lithium-ion batteries almost exclusively power today’s electric vehicles (EVs). Cutting battery costs is crucial to the promotion of EVs. This paper aims to develop potential solutions to lower the cost and improve battery performance by investigating its design variables: positive electrode porosity and thickness. The open-access lithium-ion battery design and cost model (BatPac) from the Argonne National Laboratory of the United States Department of Energy, has been used for the analyses. Six pouch battery systems with different positive materials are compared in this study (LMO, LFP, NMC 532/LMO, NMC 622, NMC 811, and NCA). Despite their higher positive active material price, nickel-rich batteries (NMC 622, NMC 811, and NCA) present a cheaper total pack cost per kilowatt-hour than other batteries. The higher thickness and lower porosity can reduce the battery cost, enhance the specific energy, lower the battery mass but increase the performance instability. The reliability of the results in this study is proven by comparing estimated and actual commercial EV battery parameters. In addition to the positive electrode thickness and porosity, six other factors that affect the battery's cost and performance have been discussed. They include energy storage, negative electrode porosity, separator thickness and porosity, and negative and positive current collector thickness.
more »
« less
- PAR ID:
- 10302618
- Date Published:
- Journal Name:
- the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Aqueous Li-ion batteries (ALIBs) are an important class of battery chemistries owing to the intrinsic non-flammability of aqueous electrolytes. However, water is detrimental to most cathode materials and could result in rapid cell failure. Identifying the degradation mechanisms and evaluating the pros and cons of different cathode materials are crucial to guide the materials selection and maximize their electrochemical performance in ALIBs. In this study, we investigate the stability of LiFePO4(LFP), LiMn2O4(LMO) and LiNi0.8Mn0.1Co0.1O2(NMC) cathodes, without protective coating, in three different aqueous electrolytes, i.e., salt-in-water, water-in-salt, and molecular crowding electrolytes. The latter two are the widely reported “water-deficient electrolytes.” LFP cycled in the molecular crowding electrolyte exhibits the best cycle life in both symmetric and full cells owing to the stable crystal structure. Mn dissolution and surface reduction accelerate the capacity decay of LMO in water-rich electrolyte. On the other hand, the bulk structural collapse leads to the degradation of NMC cathodes. LMO demonstrates better full-cell performance than NMC in water-deficient aqueous electrolytes. LFP is shown to be more promising than LMO and NMC for long-cycle-life ALIB full cells, especially in the molecular crowding electrolyte. However, none of the aqueous electrolytes studied here provide enough battery performance that can compete with conventional non-aqueous electrolytes. This work reveals the degradation mechanisms of olivine, spinel, and layered cathodes in different aqueous electrolytes and yields insights into improving electrode materials and electrolytes for ALIBs.more » « less
-
Direct recycling methods offer a non‐destructive way to regenerate degraded cathode material. The materials to be recycled in the industry typically constitute a mixture of various cathode materials extracted from a wide variety of retired lithium‐ion batteries. Bridging the gap, a direct recycling method using a low‐temperature sintering process is reported. The degraded cathode mixture of LMO (LiMn2O4) and NMC (LiNiCoMnO2) extracted from retired LIBs was successfully regenerated by the proposed method with a low sintering temperature of 300°C for 4 h. Advanced characterization tools were utilized to validate the full recovery of the crystal structure in the degraded cathode mixture. After regeneration, LMO/NMC cathode mixture shows an initial capacity of 144.0 mAh g−1and a capacity retention of 95.1% at 0.5 C for 250 cycles. The regenerated cathode mixture also shows a capacity of 83 mAh g−1at 2 C, which is slightly higher compared to the pristine material. As a result of the direct recycling process, the electrochemical performance of degraded cathode mixture is recovered to the same level as the pristine material. Life‐cycle assessment results emphasized a 90.4% reduction in energy consumption and a 51% reduction in PM2.5 emissions for lithium‐ion battery packs using a direct recycled cathode mixture compared to the pristine material.more » « less
-
The microstructural optimization of porous lithium ion battery electrodes has traditionally been driven by experimental trial and error efforts, based on anecdotal understanding and intuition, leading to the development of useful but qualitative rules of thumb to guide the design of porous energy storage technology. In this paper, an advanced data-driven framework is presented wherein the effect of experimentally accessible microstructural parameters such as active particle morphology and spacial arrangement, underlying porosity, cell thickness, etc. , on the corresponding macroscopic power and energy density is systematically assessed. For the Li x C 6 | LMO chemistry, an analysis performed on 53 356 battery architectures reported in the literature revealed that for commercial microstructures based on oblate-shaped particles, lightly textured samples deliver higher power and energy density responses as compared to highly textured samples, which suffer from large polarization losses. In contrast, high aspect ratio prolate-shaped particles deliver the highest energy and power density, particularly in the limit of wire-like morphologies. Polyhedra-based colloidal microstructures demonstrate high area densities, and low tortuosities, but provide no appreciable power and energy density benefit over currently manufactured particle morphologies. The developed framework enables to establish general microstructure design guidelines and propose optimal electrode microstructures based on the intended application, given an anode and cathode chemistry.more » « less
-
Abstract High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2(NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.more » « less
An official website of the United States government

