论文标题
通过转移增强学习的自适应能源管理针对实际驾驶条件
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
论文作者
论文摘要
本文提出了基于转移增强学习（RL）的自适应能源管理方法，用于与并行拓扑结构的混合动力汽车（HEV）。这种方法是双层的。上层表征了如何通过驱动周期转换（DCT）在RL框架中转换Q值表。特别是，针对不同循环计算了功率请求的过渡概率矩阵（TPM），并使用诱导的矩阵标准（IMN）作为关键标准来确定转换差异并确定控制策略的改变。较低的级别确定如何使用无模型的增强学习（RL）算法将相应的控制策略设置为使用转换的Q值表和TPM。数值测试表明，转移的性能可以通过IMN值调整，并且转移RL控制器可能会获得更高的燃油经济性。比较表明，在计算速度和控制性能中，提出的策略超过了常规的RL方法。
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi-level. The up-level characterizes how to transform the Q-value tables in the RL framework via driving cycle transformation (DCT). Especially, transition probability matrices (TPMs) of power request are computed for different cycles, and induced matrix norm (IMN) is employed as a critical criterion to identify the transformation differences and to determine the alteration of the control strategy. The lower-level determines how to set the corresponding control strategies with the transformed Q-value tables and TPMs by using model-free reinforcement learning (RL) algorithm. Numerical tests illustrate that the transferred performance can be tuned by IMN value and the transfer RL controller could receive a higher fuel economy. The comparison demonstrates that the proposed strategy exceeds the conventional RL approach in both calculation speed and control performance.
