Battery management systems (BMS) ensure safe and efficient operation of battery packs in electric vehicles, grid power storage systems, and other battery-driven equipment. One major task of the BMS is estimating state of charge (SoC). Traditional methods for SoC estimation require accurate battery models that are difficult to characterize. An alternative to this is to create data driven models of the cell using AI methods such as neural networks.
This webinar shows how to use Deep Learning Toolbox, Simulink, and Embedded Coder to generate C code for AI algorithms for battery SoC estimation and deploy them to an NXP S32K3 microcontroller. Based on previous work done by McMaster University on Deep Learning workflows for battery state estimation, we use Embedded Coder to generate optimized C code from a neural network imported from TensorFlow and run it in processor-in-the-loop mode on an NXP S32K3 microcontroller. The code generation workflow will feature the use of the NXP Model-Based Design Toolbox, which provides an integrated development environment and toolchain for configuring and generating all the necessary software to execute complex applications on NXP MCUs.