Lithium-ion batteries (LIBs) are vital energy storage devices for electric vehicles (EVs). Conventionally, LIBs have planar electrodes that present trade-offs between energy and power (charge/discharge speed) due to ion diffusion limitations. EVs require a high energy battery to enable long mileage ranges while also being able to charge quickly (< 15 minutes). 3D battery electrodes can potentially overcome this trade-off, achieving both high energy and power by leveraging 3D structures that create fast ion transport pathways. However, a scalable manufacturing process for 3D electrodes is needed. We are investigating processes for this, and we need a method to characterize our 3D electrodes. There is no method to automatically quantify the features within these 3D structures, which is required for rapid, high quality analysis. By accurately measuring 3D electrode feature sizes, correlations between features and optimal battery performance can be determined. We hypothesize that fabricating fine 3D features (order of 10s of microns) will improve battery performance. To address this need, I have developed an image processing script that characterizes 3D electrode samples. I investigate how threshold values improve accuracy in comparison to manual measurements and am able to achieve < 10% error. I also connect the code’s feature size measurements to our manufacturing process operating conditions to inform how manufacturing conditions can be altered to precisely control feature sizes, which impact battery performance. We expect that higher operating frequencies for our manufacturing process will result in our target fine feature 3D electrodes, achieving high-performance Lithium-ion batteries. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0010226. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
 

With recent advances in wearable technologies, there is a growing demand for high power density batteries with more complex geometries. However, conventional battery manufacturing processes such as blade casting are incapable of producing the desired complex form factors. As an alternative manufacturing method, researchers are using additive manufacturing (AM), which allows for the rapid and efficient production of complexly shaped lithium-ion batteries (LIBs). A commonly used type of AM is direct-ink write (DIW) printing, a manufacturing technique where material is extruded through a syringe using displacement-controlled mechanisms. However, DIW typically lags from when pressure is applied to the syringe to when the battery material gets dispensed onto the surface. This lag can result in the printing of inaccurate features that negatively impact battery performance or even cause device failure. To account for this lag, we created a software module using the programming language C#, allowing users to print with micron-level precision. The module was integrated into the Rhino and Grasshopper platforms, a commercial computer-aided design (CAD) software package, enabling direct application into CAD models. The module can accept a list of curves as an input and will output a transformed list of curves that are more accurate to the CAD design. This module eases the challenge of printing material at the micron level, however, further research must be conducted to implement this module into lithium-ion batteries AM.

Soft electronics are small, flexible devices that have potential in next-generation applications such as wearable electronics, robotics and biomedical devices. Additive manufacturing (AM) has been demonstrated for fabrication of electronic circuitry and insulation, but some electronic components cannot currently be made with AM. These components are typically placed onto devices by hand. We aim to develop an AM process that uses a single motion platform to switch between printing circuitry and placing electrical components without the need for manual intervention. In the pursuit of full automation, we designed and built a pick and place (PnP) tool that is compatible with the Jubilee open-source 3D printing platform, and developed software to create integrated toolpaths. The Jubilee is the ideal platform for testing multi-material 3D printing in conjunction with PnP because of its ability to switch between tool heads mid-print. Our workflow for AM of soft electronics includes 3D printing of electrical circuitry via a material extruder tool, part placement with the PnP tool, and automated switching between the tools. We have programmed software that takes user input of starting and ending component location and creates machine code. The machine code tells the Jubilee where to go and when to activate or deactivate the vacuum system. We validate the functionality of our PnP tool and software by placing components in designed patterns. We then incorporate our PnP tools into the AM process to demonstrate fully automated fabrication of a simple circuit design. Integration of our PnP tool into the Jubilee open-source platform enables circuit printing and part mounting to be executed without manual intervention and is a step towards complete automation of AM for soft electronics.
 

As the world’s reliance on Lithium-ion batteries increases for technologies like electric vehicles, we need to improve battery performance. Traditional Lithium-ion batteries are composed of planar electrodes whose thickness can be optimized for energy capacity or charge rate (power). Thinner electrodes have a faster charge/discharge rate but low energy capacity while thicker electrodes have slow charge rates but higher energy capacity. Three-dimensional (3D) electrode structures that deviate from traditional planar electrodes can mitigate these trade-offs by allowing for fast ion transport while still maintaining a high ion quantity. One structure of interest due to its theoretical performance improvements shown in literature is a line patterned electrode. Line patterned electrodes have material and structural design features that greatly impact battery performance; it is therefore important to have methods to characterize and quantify features prior to battery testing. To rapidly and accurately analyze 3D line electrode feature sizes, we have developed an image processing code that analyzes cross-sectioned images of 3D line electrodes made from battery materials, enabling automated quantification of features such as line width, spacing and height. Cross-sectioned images are converted to black and white, which can then be processed by a function to detect the line edges and calculate the feature sizes. The results of our image processing code were compared to manual measurements to quantify accuracy. We draw connections between 3D line patterned electrode features and Lithium-ion battery performance to demonstrate how 3D electrode structures can be tuned to improve performance. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0009112. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.