Energy Management Based on D4QN Reinforcement Learning for a Series-Parallel Multi-Speed Hybrid Electric Vehicle Tongji University

Format:

Book

Conference/Event

Author/Creator:

Lou, Diming, author.

Contributor:

Fang, Liang

Zhao, Yinghua

Zhuang, Caihua

Conference Name:

SAE 2023 Vehicle Powertrain Diversification Technology Forum (2023-08-26 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted. The results show that the D4QN algorithm can achieve both the approximate global optimal results, which differs from the result of dynamic programming by 0.05% under the training cycle, and establish the positive mapping relationship between state variables and action variables of the hybrid powertrain with excellent generalization ability, which can be applied to HCU and control the hybrid electric vehicle effectively in real time. Compared with the original rule-based energy management strategy for the hybrid electric vehicle, the fuel economy under WLTC cycle is improved by 9.64% after the application of D4QN energy management strategy. The proposed strategy can realize a termination of SOC (State of charge) within 50±10% under different initial SOC states for a battery of 1.8kWh, achieving the goal of SOC robustness. In addition, the proposed strategy has strong adaptability to the unknown cycles; the fuel economy of the vehicle after the application of D4QN energy management strategy in the untrained NEDC and CLTC-P cycles is improved by more than 10% compared to the WLTC cycle

Notes:

Vendor supplied data

Publisher Number:

2023-01-7007

Access Restriction:

Restricted for use by site license