Raghavendra Kurva

AI-Driven Carbon Emissions Monitoring Across the Electric Vehicle Lifecycle

Abstract:

Electric vehicles (EVs) are increasingly adopted to reduce transportation emissions, yet their overall carbon footprint spans multiple lifecycle stages including manufacturing, operation, and end-of-life. Accurately measuring emissions across these stages remains a challenge due to the complexity of distributed data and the limitations of traditional lifecycle assessment methods, which often rely on static assumptions and generalized datasets. 

This session presents an AI-driven platform designed to continuously monitor and analyze carbon emissions across the full EV lifecycle. The approach integrates artificial intelligence with lifecycle assessment frameworks to process data from supply chains, manufacturing systems, vehicle telemetry, and recycling processes. During manufacturing, AI models evaluate supplier inputs, material sourcing, and production conditions to identify emission-intensive components, with particular attention to battery production involving materials such as lithium, cobalt, and nickel. In the operational phase, real-time telemetry data including energy consumption, driving patterns, and charging behavior is used to dynamically assess emissions based on the electricity grid mix. 

At end-of-life, the platform analyzes recycling efficiency and material recovery processes to evaluate their impact on overall emissions and support circular economy strategies. A key innovation lies in unifying fragmented lifecycle data into a continuously updated monitoring system, enabling stakeholders to access real-time insights through actionable dashboards. 

By transforming emissions assessment from a static exercise into a dynamic, data-driven process, this framework supports more accurate, transparent, and accountable carbon management. It enables manufacturers, fleet operators, policymakers, and consumers to make informed decisions that improve sustainability outcomes across the EV ecosystem.

Profile:

Raghavendra Kurva is a results-driven Senior Data Engineer with over 10 years of experience designing, building, and managing scalable data solutions across cloud and on-premise environments. He has a strong background in big data technologies, ETL frameworks, data warehousing, and performance optimization, with proven success in transforming complex data into actionable business insights. 

Currently serving as a Senior Data Engineer at IptiQ America Inc., Raghavendra plays a key role in migrating legacy insurance systems to modern cloud-based architectures using Microsoft Azure. He has led large-scale data migrations with 100% accuracy and SLA compliance,

optimized Azure Data Factory pipelines to improve processing speeds by over 35%, and implemented secure, reliable data integration solutions. His expertise includes Snowflake data modeling, real-time data streaming, CI/CD automation, and maintaining high system availability in enterprise environments. 

Previously, Raghavendra spent nearly eight years at Urban Science, where he worked extensively in the automotive domain as a Software Engineer. There, he specialized in ETL automation, SQL performance tuning, database migrations, and business intelligence solutions. He significantly enhanced system performance through advanced query optimization, indexing strategies, and report optimization, reducing processing times from hours to minutes. He also led the development of internal platforms, BI dashboards, and automated testing frameworks, contributing to improved operational efficiency and software quality. 

Raghavendra holds a Master’s degree in Computer Science from Chicago State University and a Bachelor’s degree in Computer Science Engineering from JNTUH, India. His domain experience spans the insurance and automotive industries, and he is recognized for his technical leadership, problem-solving ability, and commitment to delivering high-quality, scalable data solutions.