DevConf.IN 2026

Accurate prediction of the battery range of electric vehicles requires periodic update of the prediction model as there are changes in battery parameters with time and variation in driving dynamics. Federated Learning (FL) offers the following two advantages for model update: (1) It aggregates learnings from data patterns of fleet of vehicles to provide a sophisticated model that has been trained on wide range of scenarios. (2) It protects the privacy of the vehicle user without sending raw data to the central repository for model updates. With simulated vehicle data and Flower FL framework, a range prediction solution has been developed in a manner so as to easily port to an embedded edge Texas Instruments platform. The edge component can run as a quality managed (QM) component where as the central model aggregation can run as a containerized application on-prem or cloud where communication is established using gRPC.