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.
