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A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications

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  • Xu, Bin
  • Li, Xiaoya

Abstract

In recent years, the organic Rankine cycle waste heat recovery (ORC-WHR) technology gains popularity in heavy-duty diesel engine applications. Drastic fluctuations of the waste heat caused by variable daily operation of mobile heavy-duty trucks bring an extreme transient power optimization challenge to ORC-WHR systems. Existing power optimization methods either neglect transient behavior of the Rankine cycle system or compromise model accuracy for computation efficiency. Different from literature, this study first time proposes a model-free reinforcement learning method to achieve online transient power optimization for the ORC-WHR system and explains the benefits of learning method in this application. A tabular Q-learning is formulated to optimize the net power on an experimentally validated ORC-WHR system. Q-learning is explained in detail using states, action, and policy information. To quantify the power optimization of the proposed method, Proper-Integral-Derivative method, state-of-art offline and online Dynamic Programming methods are implemented. The results showed that Q-learning generated 22% more cumulative energy than the energy Proper-Integral-Derivative method generated. Furthermore, Q-learning produces 96.6% of cumulative energy that the offline Dynamic Programming generates over a transient engine condition, while it requires less computation cost and is executed online. Additionally, the Q-learning produces 0.5% more cumulative energy than the machine learning-based online Dynamic Programming results and exhibits better vapor temperature robustness than the online Dynamic Programming method (4 °C-28 °C superheat by Q-learning vs. 5 °C-94 °C superheat by online Dynamic Programming). Given the excellent power production performance, low computation cost requirement and high robustness, the proposed Q-learning method has the potential to improve the power production of the ORC-WHR system with different configurations.

Suggested Citation

  • Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s0306261921000878
    DOI: 10.1016/j.apenergy.2021.116532
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    References listed on IDEAS

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