simulink을 활용 시뮬레이션 검증에 따른 하이브리드 전기차 강화학습 연구

simulink활용 하이브리드 전기차

Zehui Kong , Yuan Zou , Teng Liu 지음

아진 2020년 07월 03일 출간

작품소개

책소개

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.

목차

제 1편 : SIMULINK 기본편

1.1 SIMULINK의 시작 1
블록의 연결 5
블록 파라미터의 설정 7
시뮬레이션 파라미터 (Configuration Parameters)의 설정 8
시뮬레이션의 수행 9
블록 파라미터의 표시 9
복수 데이터의 표시 11

2.2 동적 시뮬레이션 13
이차 미분방정식 17
선형 상태변수 모델 23
DC 모터의 시뮬레이션 24
함수 블록의 사용 29
차분방정식(difference equation)의 모델링 34
Subsystem(부시스템)의 구성 37


제 2편 : 연구논문


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

인물정보

저자(글) Zehui Kong

저자(글) Yuan Zou

저자(글) Teng Liu