Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Intermolecular, Inc.

Abraham S Anapolsky

Patrick Kenichi Herring

Muratahan Aykol

Joseph Harold Montoya

Chirranjeevi Balaji Gopal

Minh Le

Jeroen Van Duren

A superconducting circuit element includes a piezoelectric or ferroelectric thin film formed on a rigid substrate. The piezoelectric or ferroelectric thin film has one or more circuit element openings and an under compression room temperature superconductor material disposed within the one or more circuit element openings. Actuation of the thin film applies a compressive stress on the under compression room temperature superconductor material such that a room temperature superconducting circuit element is formed on the rigid substrate.

Room temperature superconducting circuit elements and methods of manufacturing the same

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.

Systems, methods, and storage media for predicting a discharge profile of a battery pack

In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.

Vehicle battery analysis system

A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.

System and method for controlling a battery management system

A high surface area to mass catalyst is formed by a method that includes a Kirigami mapped cutting of a flat three metal laminate composite formed on a deposition support. Kirigami derived catalyst has a shape that provides a high surface to mass ratio and promotes the flow of a fluid containing a reagent for a reaction catalyzed by the exterior metal catalyst films of the three metal laminate composite. Structural integrity of the Kirigami derived catalyst results from a support metal film residing between two metal catalyst films. The shaping to the Kirigami derived structure involves cutting the flat three metal laminate composite to that of a Kirigami map, imposing stress on the cut structure to force a non-planar deformation, and delaminating the Kirigami derived catalyst from the deposition support.

Kirigami derived metal catalysts

Motor vehicles use biomagnetic sensors to determine global position by detecting Earth's geomagnetic field. Magnetosensitive bioparticles, such as magnetotactic bacteria or organelles from a magnetoreceptive eukaryotic species, are dispersed in a gel medium. The magnetosensitive bioparticles generate a local magnetic moment that changes in response to fluctuations in the immediate geomagnetic field vector, the latter of which is dependent on global position. An integrated circuit, such as an array of superconducting quantum detects changes in the local magnetic moment, and a controller thereby determines the ambient geomagnetic field vector. The controller accesses a database having a correlation of geomagnetic field vector and geolocation.

Biomagnetic sensor

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.

Systems, methods, and storage media for determining a target battery charging level for a drive route

2-manifold structures are structures having continuously curvilinear surfaces, with no kinks or sharp angles. Methods for building strong, lightweight 2-manifold structures include a computer implemented strength optimization process. The computer implemented process includes a structure optimization sub-routine in which a plurality of rod-like members, constituting an irregular scaffold, is constrained into a volume corresponding to the 2-manifold structure. The strength of the irregular scaffold is then optimized by application of an algorithm that maximizes strength as a function of variations in the ratio of the largest macroscopic dimension of the panel to the average length of the members; the aspect ratio of the members (diameter divided by length); the average or maximum number of times a member can contact another member; and the distribution of member lengths. Strength maximization can be simultaneous to minimization of total length of the members. The process can includes additional compositional and structural optimizations, prior to fabrication of the irregular scaffold, and encasing of the scaffold in a shell, or infiltration of the scaffold with a matrix material.

Ultra-strong 2-manifold panels based on biomimetic inspired, algorithmically optimized structures

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.

Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack

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.

Systems, methods, and storage media for adapting machine learning models for optimizing performance of a battery pack

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.

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San Mateo, CA, United States of America

Abraham S Anapolsky