Building Battery State-of-Health Estimation Pipelines for Electrified Vehicles

This talk gives an overview of battery state-of-health (SOH) estimation and prognostics modeling that uses data generated from the vehicle model in the cloud. The vehicle model is comprised of a Simulink^® based electric vehicle model that includes Li-Ion cell chemistry-based battery models. While building battery state-of-health pipelines, it is difficult to capture real data from the vehicle in various driving conditions. We took the approach to leverage a calibrated Li-Ion Cell Chemistry model to generate the required data in various driving conditions. We pushed this data to the cloud, then had the data pipelines pick this data and do all the downstream processing. This enabled us to build the data pipelines and the analytics stack without having extensive vehicle data. As we have now started getting real data, we are validating this analytics stack. This talk also discusses leveraging the Simulink code-generation feature to generate C-code and its feasibility for real-time in-vehicle SOH estimation.