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Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery
یادگیری تقویتی مبتنی بر معماری هوشمند مدیریت انرژی برای ماشین آلات ساختمانی ترکیبی-2020 Power allocation is of crucial significance to energy management system in the hybrid construction machinery
(HCM). Most of the existing HCM energy management strategies are only formulated based on the predefined
rules, which causes the system unable to adapt to the changeable and complicated working conditions, thus
seriously limiting the energy saving potential of hybrid technology. In this paper, we build a reinforcement
learning-based intelligent energy management architecture for HCM. Given the working conditions and operating
characteristics of HCM, a Q-function updating method combining direct learning and indirect learning is
proposed to enhance the performance and practicability of reinforcement learning. A virtual world model
(VWM) is introduced to approximate the real-world environment and facilitate the identification of data-driven
environment, so as to enhance the real-time performance and adaptability of the architecture. Based on the
characteristics of HCM working conditions, the load cycle is subdivided, and the stationary Markov chain is
employed to yield real-time transfer probability matrices of required power to accelerate the updating of the
environment model. An HCM experiment platform is built, in which the typical signal of working condition is
sampled for simulation. The results indicate that DYNA-Q based architecture outperforms Q-learning and rulebased
strategy (RBS) in terms of adaptivity, real-time performance and optimality. The results also demonstrate
that with the proposed architecture, the working condition of internal combustion engine (ICE) and the chargedischarge
of ultracapacitor are more rational and efficient. Keywords: Hybrid construction machinery | Energy management | Reinforcement learning | Dyna-Q learning | Virtual world model |
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