Volkswagen Ag

Audi Ag

Dr. Ing. H.c. F. Porsche Aktiengesellschaft

Porsche Ag

Melanie Senn

Nasim Souly

Gianina Alina Negoita

Joerg Christian Wolf

Elnaz Vahedforough

Nils Kuepper

Mohak Prafulkumar Bhimani

Vedran Glavas

Julian Wegener

Prateek Agrawal

Ulrich Maehr

Lorenz Haghenbeck Emde

Nils KÜPPER

In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

Determining material properties based on machine learning models

A powertrain system may determine a power distribution for a set of power sources of a vehicle. The powertrain system may be coupled to a perception system that may provide perception data indicating a scenario, situation, or environment that has been encountered by the vehicle. The powertrain system may also receive health values. The powertrain system may include machine learning model that may generate the power distribution based on one or more of the perception data, the health values, and a power request.

Powertrain control system with state of health information

Disclosed embodiments provide a technical improvement for providing localization for a transportation vehicle by detecting road wear reference lines in a roadway on which the transportation vehicle is travelling and controlling, guiding or otherwise facilitating alignment of the transportation vehicle wheel centers with the detected centers of the road wear.

On-road localization methodologies and equipment utilizing road surface characteristics

A system and a method are provided for compressing a deep neural network ('DNN'). In some examples, the DNN is trained, where the DNN has at least one layer having multiple filters. Clustering of the filters of at least one layer is performed. Dimension reduction can be applied as well to the filters to reduce the channel dimensionality of the at least one layer. The dimensionally reduced DNN can then be retrained. Once retrained, the compressed DNN can be stored in a storage device.

System and method for deep neural network compression

A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence ('AI') model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Battery materials screening

A computing system, method, and apparatus for determining a state of health indication for a battery are provided. A first supervised deep neural network ('DNN') is trained using received characteristics for the battery as input and received process parameters as outputs. An unsupervised AI estimator is trained using one or more clustering methods based on extracted features from the first supervised DNN, where the received characteristics are input to the unsupervised AI estimator. A second supervised DNN is trained using identified clusters from the unsupervised AI estimator. The identified clusters are validated with state of health indications. User battery data is inputted to the second supervised DNN to determine the state of health for the battery.

Growing community of inventors

Mountain View, CA, United States of America

Melanie Senn