Developing intelligent energy management systems with high adaptability and superiority is necessary and significant for Hybrid Electric Vehicles (HEVs). This paper proposed an ensemble learning-based scheme based on a learning automata module (LAM) to enhance vehicle energy efficiency. Two parallel base learners following two exploration-to-exploitation ratios (E2E) methods are used to generate an optimal solution, and the final action is jointly determined by the LAM using three ensemble methods. 'Reciprocal function-based decay' (RBD) and 'Step-based decay' (SBD) are proposed respectively to generate E2E ratio trajectories based on conventional Exponential decay (EXD) functions of reinforcement learning. Furthermore, considering the different performances of three decay functions, an optimal combination with the RBD, SBD, and EXD is employed to determine the ultimate action. Experiments are carried out in software-in-loop (SiL) and hardware-in-the-loop (HiL) to validate the potential performance of energy-saving under four predefined cycles. The SiL test demonstrates that the ensemble learning system with an optimal combination can achieve 1.09$\%$ higher vehicle energy efficiency than a single Q-learning strategy with the EXD function. In the HiL test, the ensemble learning system with an optimal combination can save more than 1.04$\%$ in the predefined real-world driving condition than the single Q-learning scheme based on the EXD function.

翻译：开发具有高适应性和优越性的智能能量管理系统对于混合动力电动汽车（HEVs）而言至关重要且意义重大。本文提出一种基于学习自动机模块（LAM）的集成学习方案，以提升车辆能量效率。采用两种遵循不同探索与利用比率（E2E）方法的并行基学习器生成最优解，最终动作由LAM通过三种集成方法联合确定。分别提出"基于倒数函数的衰减"（RBD）和"基于步长的衰减"（SBD）方法，以基于强化学习传统指数衰减（EXD）函数生成E2E比率轨迹。此外，综合考虑三种衰减函数的不同性能，采用RBD、SBD与EXD的最优组合来确定最终动作。通过软件在环（SiL）与硬件在环（HiL）实验，在四种预定义循环工况下验证节能潜力。SiL测试表明，采用最优组合的集成学习系统相比基于EXD函数的单一Q学习策略，车辆能量效率提升1.09%。HiL测试中，在预定义真实驾驶工况下，采用最优组合的集成学习系统比基于EXD函数的单一Q学习方案节能超过1.04%。