# Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2408.02022 · HTML: https://mlsanity.com/paper/2408.02022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 4 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning](https://mlsanity.com/paper/2110.02034/) (2021)
- [PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning](https://mlsanity.com/paper/2205.07000/) (2022)
- [Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach](https://mlsanity.com/paper/2203.09661/) (2022)
- [ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries](https://mlsanity.com/paper/2401.05251/) (2024)

## Abstract (arXiv)

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.