  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.
