Summary of Constrained Optimal Fuel Consumption Of Hev:considering the Observational Perturbation, by Shuchang Yan and Haoran Sun
Constrained Optimal Fuel Consumption of HEV:Considering the Observational Perturbation
by Shuchang Yan, Haoran Sun
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper tackles the Constrained Optimal Fuel Consumption (COFC) problem using Constrained Reinforcement Learning (CRL), which requires accurate observation of battery state of charge (SOC) and precise speed curves. However, in reality, SOC measurements are often distorted by noise or confidentiality protocols, and actual reference speeds may deviate from expectations. To address this issue, the paper aims to minimize fuel consumption while maintaining SOC balance under observational perturbations in SOC and speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The COFC problem is addressed using seven training approaches that can successfully solve the problem under five types of perturbations. The methods include one based on a uniform distribution, one designed to maximize rewards, one aimed at maximizing costs, and one along with its improved version that seeks to decrease reward on Toyota Hybrid Systems (THS) under New European Driving Cycle (NEDC) condition. |
Keywords
* Artificial intelligence * Reinforcement learning
