Channel: MATLAB

Category: Science & Technology

Tags: matlabsimulinkmathworks6302924635001

Description: Watch a quick demonstration of how to use a pretrained TensorFlow™ network in Simulink® to implement a deep learning-based, state-of-charge estimation algorithm for a battery management system. This demo uses a neural network that has been trained in TensorFlow using battery discharge data measured in the lab. The example has two parts: importing a pretrained TensorFlow model into MATLAB® and using the imported model in Simulink for simulation and library-free C code generation. The first part shows how to use the importTensorFlowNetwork command to bring a neural network into MATLAB from TensorFlow and how to visualize an imported network in Deep Network Designer. The second part illustrates how to put an imported network into a Simulink model using Predict block. Using this block, the network is simulated and results are compared with the true state-of-charge level as well as an estimate obtained using an extended Kalman Filter. Finally, the imported network is used to generate library-free C code that can run on any microcontroller or ECU, including the NXP S32K boards. MATLAB for Deep Learning: bit.ly/38iFojo Deep Learning in Simulink. Simulating AI within large complex systems: bit.ly/3Lirah9 -------------------------------------------------------------------------------------------------------- Get a free product trial: goo.gl/ZHFb5u Learn more about MATLAB: goo.gl/8QV7ZZ Learn more about Simulink: goo.gl/nqnbLe See what's new in MATLAB and Simulink: goo.gl/pgGtod © 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.