Zehui Kong, Yuan Zou, Teng Liu |

- 2020-07-13

- 12 M

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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.

1 : SIMULINK ⺻

1.1 SIMULINK 1

5

Ķ 7

ùķ̼ Ķ (Configuration Parameters) 8

ùķ̼ 9

Ķ ǥ 9

ǥ 11

2.2 ùķ̼ 13

̺й 17

º 23

DC ùķ̼ 24

Լ 29

й(difference equation) 34

Subsystem(νý) 37

2 :

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