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Developing intelligent energy management systems with high adaptability andsuperiority is necessary and significant for Hybrid Electric Vehicles (HEVs).This paper proposed an ensemble learning-based scheme based on a learningautomata module (LAM) to enhance vehicle energy efficiency. Two parallel baselearners following two exploration-to-exploitation ratios (E2E) methods areused to generate an optimal solution, and the final action is jointlydetermined by the LAM using three ensemble methods. 'Reciprocal function-baseddecay' (RBD) and 'Step-based decay' (SBD) are proposed respectively to generateE2E ratio trajectories based on conventional Exponential decay (EXD) functionsof reinforcement learning. Furthermore, considering the different performancesof three decay functions, an optimal combination with the RBD, SBD, and EXD isemployed to determine the ultimate action. Experiments are carried out insoftware-in-loop (SiL) and hardware-in-the-loop (HiL) to validate the potentialperformance of energy-saving under four predefined cycles. The SiL testdemonstrates that the ensemble learning system with an optimal combination canachieve 1.09$\%$ higher vehicle energy efficiency than a single Q-learningstrategy with the EXD function. In the HiL test, the ensemble learning systemwith an optimal combination can save more than 1.04$\%$ in the predefinedreal-world driving condition than the single Q-learning scheme based on the EXDfunction.

Optimal Energy Management of Plug-in Hybrid Vehicles Through Exploration-to-Exploitation Ratio Control in Ensemble Reinforcement Learning