2026-02-13 –, VYAS - G - Room#VY016
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.
An SDV & Automotive IoT evangelist and a firm believer of open source technologies impact on our lifestyle.
Vinod is experienced in architecting and delivering AI/ML based solutions for customers in automotive, energy, healthcare, retail and consumer electronics. His current interest is on distributed LLM inferencing using cloud native frameworks and edge computing. His contributions in automating repetitive human tasks using predictive and generative AI has led to significant savings in effort and improved safety. He has led a number of PoCs and production deployments of AI/ML-based systems on cloud, cars, trucks, drones and wearable devices. He is passionate about modernization of enterprise IT and OT workflows using AI.
Vinod has been a speaker at multiple conferences and workshops and has contributed as a reviewer and member of technical program committees. He is the inventor of 25 patents filed (of which 19 were granted) and has authored more than 15 peer-review papers and business articles. He is an open source contributor and mentors aspiring engineers.