Race smart, last longer: deep learning approaches for li-ion battery state estimation and autonomous racing vehicles

As the demand for advanced autonomous systems grows, reliable energy storage and management becomes paramount, especially in high-performance contexts such as autonomous racing vehicles—including cars and drones. This dissertation explores two interrelated topics: (1) the accurate estimation and prediction of Lithium-ion (Li-ion) battery states, and (2) the development of Autonomous Vehicles (AVs) in competitive racing environments. The integration of these topics underscores the pivotal role that energy management plays in maximizing autonomous systems’ efficiency, safety, and performance. The first subject addresses the challenges associated with the state estimation of Li-ion batteries, which are the cornerstone of energy storage in modern autonomous systems. Accurate state estimation is critical for ensuring the longevity, reliability, and optimal performance of these batteries, particularly in applications where they are exposed to extreme operational stress. Through the application of deep learning, this research improves the accuracy of battery state estimation/prediction. The second subject focuses on the development of AVs in the context of racing. Racing provides a unique testing ground for autonomous systems, where rapid decision-making and precise control are critical. This research contributes to the field by developing reinforcement learning techniques for autonomous driving. Additionally, a novel dataset for autonomous drone racing is introduced, which provides a benchmark for high-speed navigation tasks. The third subject explores the potential for integration between these two domains, which is the unifying theme presented throughout the study. This part highlights the symbiotic relationship between battery performance and vehicle control policies' success. Overall, this dissertation makes contributions to both fields. It presents open-source software and publicly available datasets that support the research community in advancing these domains. These findings lay the groundwork for future research into the convergence of energy management and autonomous system design, promising further innovations in the pursuit of more sustainable and capable autonomous technologies.