2024/08/04 by Thomas Rudolf, Philip Muhl, Rudolf, Thomas +5
Engineering · #Artificial Intelligence (cs.AI) #Building Energy and Comfort Optimization #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Heat Transfer and Optimization #Machine Learning (cs.LG) #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2408.02022
openalex publication_date 2024/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The thermal system of battery electric vehicles demands advanced control. Its thermal management needs to effectively control active components across varying operating conditions. While robust control function parametrization is required, current methodologies show significant drawbacks. They consume considerable time, human effort, and extensive real-world testing. Consequently, there is a need for innovative and intelligent solutions that are capable of autonomously parametrizing embedded controllers. Addressing this issue, our paper introduces a learning-based tuning approach. We propose a methodology that benefits from automated scenario generation for increased robustness across vehicle usage scenarios. Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets. We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing. The results highlight the competitive performance to baseline methods. This novel approach contributes to the shift towards virtual development of thermal management functions, with promising potential of large-scale parameter tuning in the automotive industry.