Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach for autonomous thermal management parameter tuning in electric vehicles, enabling robust, efficient, and virtualized controller optimization across diverse scenarios.

Contribution

It introduces a novel learning-based methodology with automated scenario generation and image-based parameter interpretation for thermal controller tuning.

Findings

Demonstrates effectiveness in valve controller parametrization

Achieves competitive performance with baseline methods

Validated in real-world vehicle testing

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.