Scenario-Based Thermal Management Parametrization Through Deep Reinforcement Learning

IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 2088–2095

Abstract

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.

Authors 4

1. Thomas Rudolf
   Affiliation as printed

   Porsche Engineering Group GmbH,department for Software Calibration and Diagnosis,Weissach,Germany,71287

2. Philip Muhl
   Affiliation as printed

   Dr. Ing. h.c. F. Porsche AG,department for Thermal Management Systems,Weissach,Germany,71287

3. Sören Hohmann

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute of Control System (IRS), Karlsruhe Institue of Technology (KIT),Karlsruhe,Germany,76131

4. Lutz Eckstein Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen,Aachen,Germany,52056