Summary of Constrained Optimal Fuel Consumption Of Hev: a Constrained Reinforcement Learning Approach, by Shuchang Yan
Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approach
by Shuchang Yan
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a novel approach to optimize the fuel consumption of hybrid electric vehicles (HEVs) using constrained reinforcement learning (CRL). The authors propose a mathematical expression for constrained optimal fuel consumption (COFC) and utilize two mainstream CRL approaches, constrained variational policy optimization (CVPO) and Lagrangian-based approaches, to obtain the minimum fuel consumption under battery electrical balance conditions. Case studies are conducted on the Toyota Prius hybrid system under the New European Driving Cycle (NEDC) condition, demonstrating the effectiveness of the proposed methods in achieving the lowest fuel consumption while maintaining SOC balance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making cars more energy-efficient. Hybrid electric vehicles combine the benefits of gasoline and electric engines. The authors want to find the most efficient way for these cars to use fuel while keeping their batteries balanced. They use a new method called constrained reinforcement learning (CRL) to do this. Two ways of using CRL are tested, and one method is found to be better at reducing fuel consumption than the other. This research helps us make hybrid electric vehicles more energy-efficient. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
