Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

Teng Liu; Bin Tian; Yunfeng Ai; Fei-Yue Wang

doi:10.1109/JAS.2020.1003072

Volume 7 Issue 2

Mar. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(2): 617-626

Teng Liu, Bin Tian, Yunfeng Ai and Fei-Yue Wang, "Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 617-626, Mar. 2020. doi: 10.1109/JAS.2020.1003072

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Teng Liu, Bin Tian, Yunfeng Ai and Fei-Yue Wang, "Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 617-626, Mar. 2020. doi: 10.1109/JAS.2020.1003072

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Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

doi: 10.1109/JAS.2020.1003072

Funds: The work was supported in part by the National Natural Science Foundation of China (61533019, 91720000), Beijing Municipal Science and Technology Commission (Z181100008918007), and the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (pICRI-IACVq)

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Author Bio:
Teng Liu (M’18) received the B.S. degree in mathematics from Beijing Institute of Technology, in 2011. He received the Ph.D. degree in automotive engineering from Beijing Institute of Technology (BIT), in 2017. He worked as a Research Fellow at Vehicle Intelligence Pioneers Ltd. for one year. Now, he is a member of IEEE VTS, IEEE ITS, IEEE IES, IEEE TEC. Dr. Liu is now a Professor in the Department of Automotive Engineering, Chongqing University. Dr. Liu has more than 8 years’ research and working experience in renewable vehicle and connected autonomous vehicle. His research interests include reinforcement learning (RL)-based energy management in hybrid electric vehicles, RL-based decision making for autonomous vehicles, and CPSS-based parallel driving. He has published over 30 SCI papers and 10 conference papers in these areas. He received the Merit Student of Beijing in 2011, the Teli Xu Scholarship (Highest Honor) of Beijing Institute of Technology in 2015, “Top 10” in 2018 IEEE VTS Motor Vehicle Challenge and sole outstanding winner in 2018 ABB Intelligent Technology Competition

Bin Tian (M’18) received the B.S. degree from Shandong University, in 2009 and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2014. He is currently an Associate Professor of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interests include computer vision, machine learning, and automated driving

Yunfeng Ai (M’18) received the B.S. degree from Shandong University, in 2001 and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2006. He was a Visiting Scholar from December 2015 to October 2016 and Postdoctoral Researcher from November 2016 to April 2017 at Carnegie Mellon University. He is currently a Research Scientist of University of Chinese Academy of Sciences. His research interests include computer vision, machine learning, parallel robots, and automated driving

Fei-Yue Wang (S’87–M’89–SM’94–F’03) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). Dr. Wang’s research focuses on methods and applications for parallel systems, social computing, and knowledge automation. Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014
Corresponding author: B. Tian is with the Vehicle Intelligence Pioneers Inc., Qingdao 266109, and also with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (e-mail: bin.tian@ia.ac.cn)
Received Date: 2018-07-05
Revised Date: 2018-10-10
Accepted Date: 2018-12-12

Abstract

Abstract

As a complex and critical cyber-physical system (CPS), the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy (EMS) is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory (LSTM) network based parallel reinforcement learning (PRL) approach to construct EMS for a hybrid tracked vehicle (HTV). This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning (RL) framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
- Bidirectional long short-term memory (LSTM) network,
- cyber-physical system (CPS),
- energy management,
- parallel system,
- reinforcement learning (RL)

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References

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Parallel reinforcement learning was used to construct energy management strategy.
A parallel system including a real powertrain and an artificial system was built.
Data from the parallel system is trained by a bidirectional long short-term network.
Real vehicle testing is implemented and experiment data is collected and calibrated.

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

doi: 10.1109/JAS.2020.1003072

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