Abstract: Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

Abstract: Systems, methods, and storage media for determining a target charging level of a battery pack for a drive route are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet of vehicles, providing the data to a machine learning server, directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, receiving a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route, estimating travel conditions of the vehicle based on the route request, determining a temperature of the battery pack in the vehicle, determining a target battery charging level based on the predictive model, the travel conditions, and the temperature, and providing the target battery charging level to the vehicle.

Abstract: A method for additive manufacturing a three-dimensional article may comprise simulating the three-dimensional article based on at least one parameter characterizing the three-dimensional article such that the three-dimensional article comprises a first region comprising a first base material and a second region comprising a second base material. The method may further comprise generating a set of print instructions based on a conformation of the three-dimensional article, preparing an additive manufacturing feedstock comprising the first base material and the second base material based on the three-dimensional article, and supplying the set of print instructions and the additive manufacturing feedstock to an additive manufacturing device. The additive manufacturing device may then fabricate the three-dimensional article using the additive manufacturing feedstock based on the set of print instructions.

Abstract: Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and one or more battery testing devices, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, estimating, by the processing device, one or more electrical characteristics of each cell to be included in the vehicle battery pack based on the predictive model, and directing, by the processing device, an arrangement of the cells within the battery pack based on the electrical characteristics.

Abstract: Systems, methods, and storage media for optimizing performance of a vehicle battery pack are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle, providing the data to a machine learning server, and directing the machine learning server to generate a predictive model. The predictive model is based on machine learning of the data. The method further includes providing the predictive model to each vehicle, the predictive model providing instructions for adjusting configuration parameters for each of the cells in the battery pack such that the battery pack is optimized for a particular use, and directing each vehicle to optimize performance of the vehicle battery pack based on the predictive model.

Abstract: Systems, methods, and storage media for determining a target charging level of a battery pack for a drive route are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet of vehicles, providing the data to a machine learning server, directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, receiving a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route, estimating travel conditions of the vehicle based on the route request, determining a temperature of the battery pack in the vehicle, determining a target battery charging level based on the predictive model, the travel conditions, and the temperature, and providing the target battery charging level to the vehicle.

Abstract: Systems, methods, and storage media for optimizing performance of a vehicle battery pack are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle, providing the data to a machine learning server, and directing the machine learning server to generate a predictive model. The predictive model is based on machine learning of the data. The method further includes providing the predictive model to each vehicle, the predictive model providing instructions for adjusting configuration parameters for each of the cells in the battery pack such that the battery pack is optimized for a particular use, and directing each vehicle to optimize performance of the vehicle battery pack based on the predictive model.

Abstract: Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

Abstract: Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and one or more battery testing devices, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, estimating, by the processing device, one or more electrical characteristics of each cell to be included in the vehicle battery pack based on the predictive model, and directing, by the processing device, an arrangement of the cells within the battery pack based on the electrical characteristics.

Abstract: Provided are inverse phase allotrope rare earth (IPARE) magnets, methods of forming thereof, and applications of IPARE magnets. Unlike conventional samarium-cobalt magnets, IPARE magnets maintain their hexagonal lattice structures over a range of equiatomic compositions, such as when concentrations of different elements are within 10 atomic % of each other. An IPARE magnet may comprise cobalt, iron, copper, nickel, and samarium and a concentration of cobalt may be between 17-27 atomic %. An IPARE magnet may be substantially free from zirconium and/or titanium. An IPARE magnet may be formed by quenching a molten mixture of its components. The quenching may be performed in a magnetic field. After quenching, the IPARE magnet may be machined. Furthermore, IPARE magnets may be used as a structural element, e.g. in an electric motor.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A first current collector is provided. A first layer is formed above the first current collector. The first layer includes lithium and cobalt. The first layer is annealed. A second layer is formed above the annealed first layer. The second layer includes lithium and cobalt, and the annealed first layer and the second layer jointly form a first electrode. An electrolyte is formed above the first electrode. A second electrode is formed above the electrolyte. A second current collector is formed above the second electrode.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A first current collector is provided. A first layer is formed above the first current collector. The first layer includes lithium and cobalt. The first layer is annealed. A second layer is formed above the annealed first layer. The second layer includes lithium and cobalt, and the annealed first layer and the second layer jointly form a first electrode. An electrolyte is formed above the first electrode. A second electrode is formed above the electrolyte. A second current collector is formed above the second electrode.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A layer stack may be formed between a substrate of the batteries and a current collector of the batteries. A texturing may be provided to at least one of the components of the batteries to increase the interfacial area between the components. At least one of conductive metal oxides, conductive metal nitrides, conductive metal carbides, or a combination thereof may be used to form a current collector of the batteries.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A first current collector is provided. A first electrode is formed above the first current collector. The first electrode has at least one void formed therein. A fluidic, ionically-conductive material is infused into the at least one void within the first electrode. A solid electrolyte is formed above the first electrode. A second electrode is formed above the solid electrolyte. A second current collector is formed above the second electrode.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A first current collector is provided. A first electrode is formed above the first current collector. The first electrode includes chromium and manganese and is formed using PVD. An electrolyte is formed above the first electrode. A second electrode is formed above the electrolyte. A second current collector is formed above the second electrode.

Abstract: Embodiments provided herein describe solid-state lithium batteries and methods for forming such batteries. A first current collector is provided. A first electrode is formed above the first current collector. The first electrode includes lithium and cobalt and is formed using PVD in a gaseous environment including at least 96% argon. An electrolyte is formed above the first electrode. A second electrode is formed above the electrolyte. A second current collector is formed above the second electrode.

Abstract: Embodiments provided herein describe low-e panels utilizing high-entropy alloys (HEAs) and methods for forming such low-e panels, as well as combinatorial methods and systems for developing such low-e panels. A transparent substrate is provided. A reflective layer is formed above the transparent substrate. A metallic layer is formed above the transparent substrate. The metallic layer includes an HEA. The metallic layer, or any other component of the low-panels, may be formed using combinatorial processing.

Abstract: Methods, and coated panels fabricated from the methods, are disclosed to form multiple coatings, (e.g., one or more infrared reflective layers), with minimal color change before and after heat treatments. The optical properties of the coating (e.g. the transmissivity and the IR emissivity) are generally coupled. In some embodiments, silicate materials are doped with rare earth elements. These doped silicate materials are able to absorb ultra-violet (UV) photons and emit photons in the visible range. This allows the transmissivity to be at least partially decoupled from the IR emissivity of the coated panel, resulting in a larger range of performance.

Abstract: Embodiments of the present invention generally relate to lithium-ion batteries, and more specifically, to a method of fabricating such batteries using thin-film deposition processes. In one embodiment In one embodiment, a method of forming a film on a substrate is provided. The method comprises combining a lithium-containing precursor, an iron containing precursor, and an organic solvent to form a deposition mixture, optionally exposing the deposition mixture to vibrational energy, applying microwave energy to the deposition mixture to heat the deposition mixture, optionally exposing the heated deposition mixture to vibrational energy, and depositing the heated deposition mixture on a substrate to form a film comprising lithium containing nanocrystals.

Abstract: Embodiments of the invention generally relate to apparatus and methods for washing substrates. In particular, apparatus and methods for washing solar cell substrates are described, the apparatus including a detergent mixing circuit to control detergent concentration during washing.