Summary of Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning, by Thomas Rudolf et al.
Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning
by Thomas Rudolf, Philip Muhl, Sören Hohmann, Lutz Eckstein
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new learning-based tuning approach is introduced for efficiently parametrizing embedded controllers in battery electric vehicles’ thermal systems. The current methodologies for robust control function parametrization are time-consuming and require extensive real-world testing. This innovative method uses automated scenario generation to increase robustness across vehicle usage scenarios, processing the tuning task context with a deep reinforcement learning agent that incorporates an image-based interpretation of embedded parameter sets. The approach is demonstrated through a valve controller parametrization task and verified in real-world vehicle testing, showing competitive performance compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make electric cars’ cooling systems work better is being developed. Right now, it takes a lot of time and testing to get the system working right. This new approach uses artificial intelligence to help design the system, making it more efficient and effective. It works by creating different scenarios for how the car might be used and then using an image-based system to understand how the parameters work together. The results show that this method is just as good as other ways of doing things, and it has the potential to make a big impact in the automotive industry. |
Keywords
» Artificial intelligence » Reinforcement learning
