This study aims to present a method for real-time realization of Dynamic Programming algorithm for power optimization in an organic Rankine Cycle waste heat recovery system. Different from existing studies, for the first time machine learning algorithms are utilized to extract the rules from offline Dynamic Programming results for optimal power generation. In addition, for the first time a single state Proper Orthogonal Decomposition and Galerkin Projection based reduced order model is combined with Dynamic Programming for its high accuracy and low computation cost. For a transient driving cycle, Dynamic Programming algorithm is utilized to generate the optimal working fluid pump speed. A total of eleven state-of-art machine learning algorithms are screened to predict this pump speed. Random Forest algorithm is then selected for its best pump speed prediction accuracy. A rule-based method is added to the Random Forest model to improve energy recovery. As one of the main discoveries in this study, in the rule extraction process, the Random Forest model reveals that the time delayed exhaust gas mass flow rate and exhaust temperature improve the rule extraction accuracy. This observation points out the difference between steady state and transient optimization and that the steady state optimization results do not necessarily hold true in transient conditions. Another key observation is that Random Forest – rule-based method retrieves 97.2% of the energy recovered by offline Dynamic Programming in a validation driving cycle. In addition, the inclusion of rule-based method significantly increases the Random Forest model’s energy recovery from 66.5% to 97.2%. This high accuracy means that the machine learning models can be used to extract Dynamic Programming rules for real-time application.

本研究旨在提出一种实时实现有机朗肯循环余热回收系统中功率优化的动态规划算法的方法。与现有研究不同，这是首次将机器学习算法用于从离线动态编程结果中提取规则，以实现最佳发电。此外，首次将基于单状态的正确正交分解和基于Galerkin投影的降阶模型与动态规划相结合，以实现高精度和低计算成本。对于瞬态行驶周期，动态编程算法可用于生成最佳工作流体泵速度。总共筛选了11种最新的机器学习算法，以预测该泵速。然后选择随机森林算法以实现其最佳泵速预测精度。基于规则的方法被添加到随机森林模型以提高能量回收率。作为该研究的主要发现之一，在规则提取过程中，Random Forest模型显示出延迟的废气质量流量和排气温度可提高规则提取的准确性。该观察结果指出了稳态和瞬态优化之间的差异，并且稳态优化结果不一定在瞬态条件下成立。另一个关键的观察结果是，基于规则的“随机森林”方法在验证驾驶周期内，通过脱机动态编程回收了97.2％的能量。此外，包含基于规则的方法可将随机森林模型的能量回收率从66.5％提高到97.2％。这种高准确性意味着机器学习模型可用于提取动态编程规则以用于实时应用。