In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse the future power demand information into transition probability matrix (TPM) computation. Furthermore, the Kullback-Leibler (KL) divergence rate is employed to decide the alteration of control strategy. The lower-level computes the relevant energy management strategy, based on the updated TPM and a model-free reinforcement learning (RL) technique. Simulation results illustrate that the vehicular energy efficiency in the proposed scheme exceeds the common RL control by tuning the KL divergence value. Comparative results also show that the developed control strategy outperforms the common RL one, in terms of energy efficiency and computational speed.
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Teng Liu (2018) studied this question.