Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus
By Jingda Wu, Hongwen He, Jiankun Peng, Yuecheng Li, Zhanjiang Li. Applied Energy, 2018
370 citations recorded by the source corpus.
Abstract Reinforcement learning is a new research hotspot in the artificial intelligence community. Q learning as a famous reinforcement learning algorithm can achieve satisfactory control performance without need to clarify the complex internal factors in controlled objects. However, discretization state is necessary which limits the application of Q learning in energy management for hybrid electric bus (HEB). In this paper the deep Q learning (DQL) is adopted for energy management issue and the strategy is proposed and verified. Firstly, the system modeling of bus configuration are described. Then, the energy management strategy based on deep Q learning is put forward. Deep neural network is employed and well trained to approximate the action value function (Q function). Furthermore, the Q learning strategy based on the same model is mentioned and applied to compare with deep Q learning. Finally, a part of trained decision network is analyzed separately to verify the effectiveness and rationality of the DQL-based strategy. The training results indicate that DQL-based strategy makes a better performance than that of Q learning in training time consuming and convergence rate. Results also demonstrate the fuel economy of proposed strategy under the unknown driving condition achieves 89% of dynamic programming-based method. In addition, the technique can finally learn to the target state of charge under different initial conditions. The main contribution of this study is to explore a novel reinforcement learning methodology into energy management for HEB which solve the curse of state variable dimensionality, and the techniques can be adopted to solve similar problems.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/73bf64949db25fff8c2d438817e5e1543f954cbb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
Citations in the corpus
- Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, methodology): “In [26,27], RL algorithms were employed to obtain energy management plans for electric vehicles (EVs) aiming at minimizing energy losses, in which the EVs control policy was acquired through RL.”