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  • ÀúÀÚZehui Kong, Yuan Zou, Teng Liu Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-13
  • µî·ÏÀÏ2020-12-21
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To further improve the fuel economy of series hybrid electric tracked vehicles, a
reinforcement learning (RL)-based real-time energy management strategy is
developed in this paper. In order to utilize the statistical characteristics of online
driving schedule effectively, a recursive algorithm for the transition probability
matrix (TPM) of power-request is derived. The reinforcement learning (RL) is
applied to calculate and update the control policy at regular time, adapting to the
varying driving conditions. A facing-forward powertrain model is built in detail,
including the engine-generator model, battery model and vehicle dynamical model.
The robustness and adaptability of real-time energy management strategy are
validated through the comparison with the stationary control strategy based on
initial transition probability matrix (TPM) generated from a long naturalistic
driving cycle in the simulation.
Results indicate that proposed method has better fuel economy than stationary one
and is more effective in real-time control.

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1. Introduction 41
2. Modelling of hybrid electric tracked vehicle 43
3. Real-time energy management strategy 45
4. RL-based Real-time energy management strategy 46
5. Simulation and validation 48
6. Conclusion 51
7. References 55

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