AI Workflows for Battery State Estimation

State of charge (SOC) estimation is among the most important tasks of a battery management system (BMS). SOC estimation is typically performed by current integration or using a Kalman filter. In this session, we will describe an alternative method based on AI. A deep neural network is trained to predict SOC based on voltage, current, and temperature measurements. The resulting network is then implemented in Simulink^® and incorporated into a closed-loop BMS model. Finally, C code is automatically generated from the net for hardware implementation on an NXP S32K3 board used in PIL mode.