This week’s Fish Fry podcast is all about AI modeling for automotive applications.  Seth DeLand (MathWorks) and I discuss the common challenges that automotive engineers face when implementing simulation into AI modeling, which automotive applications would benefit most from simulation, and why data preparation is a crucial step in the AI workflow during model-based design for automotive applications.
Links:
More information about MATLAB – [ Ссылка ]
More information about Simulink – [ Ссылка ]
More information about RoadRunner – [ Ссылка ]
Customer Example #1: Gotion, Inc. – Using battery cell charging data stored in Gotion's cloud data platform to train and validate a neural network to estimate onboard battery pack state of charge using a trained neural network – [ Ссылка ]
Customer Example #2: KPIT – Battery state of health and state of charge estimation using a hybrid machine learning approach Customer -- [ Ссылка ]
Example #3: Stellantis – Applying AI technologies to vehicle sensor modeling -- [ Ссылка ]

свернуть