| HYPERPARAMETERS OF Q-LEARNING ALGORITHM ADAPTING TO THE DRIVING CYCLE BASED ON KL DRIVING CYCLE RECOGNITION
|
| Yanli Yin 1,2, Xuejiang Huang 1, Xiaoliang Pan 3, Sen Zhan 1, Yongjuan Ma 1, Xinxin Zhang 1 |
1School of Mechatronics & Automobile Engineering, Chongqing Jiao Tong University
2Beiben Trucks Group Co., Ltd.
3Chongqing Changan Automobile Stock Co., Ltd. |
|
|
|
|
|
|
|
| ABSTRACT |
|
As an effective reinforcement learning (RL) algorithm, Q-learning has been applied to energy management
strategy of hybrid electric vehicle (HEV) in recent years. In the existing literatures, the values of three hyperparameters based
on Q-learning are all given in advance, which are respectively exploratory rate ε, discount factor γ and learning rate α. However,
different values of hyperparameters will influence on fuel economy of the vehicle and offline computation speed. In this paper,
it is proposed that the method of optimization on hyperparameters adapting to driving cycle. Firstly, the mathematical model
between three hyperparameters and iteration times is established based on inherent regularity of hyperparameters influencing
on vehicle performance respectively. Secondly, it is determined that the optimal changing index k_index of iteration number
based on Q-learning corresponding to typical driving cycles. Finally, the simulation model of Yubei District in Chongqing is
constructed based on the method of Kullback-Leibler (KL) driving cycle identification. The simulation results indicate that
equivalent fuel consumption of the proposed strategy is reduced by 0.4 % and the offline operation time is reduced by 6 s. It
can be concluded that the proposed strategy can not only improve fuel economy of the vehicle, but also accelerate the
computation speed. |
| Key Words:
Q-Learning, Hyperparameters, Adapting to, Kullback-Leibler (KL), Computation speed, Fuel economy
|
|
|
|
Corresponding Author.
Yanli Yin , Email. 
Preview
Full text via DOI
Download Citation 
Print 
