Abstract
The energy management system of an electrified vehicle is one of the most important supervisory control systems which manages the use of on-board energy resources. This paper researches a ‘model-free’ predictive energy management system for a connected electrified off-highway vehicle. A new reinforcement learning algorithm with the capability of ‘multi-step’ learning is proposed to enable the all-life-long online optimisation of the energy management control policy. Three multi-step learning strategies (Sum-to-Terminal, Average-to-Neighbour Recurrent-to-Terminal) are researched for the first time. Hardware-in-the-loop tests are carried out to examine the control functionality for real application of the proposed ‘model-free’ method. The results show that the proposed method can continuously improve the vehicle's energy efficiency during the real-time hardware-in-the-loop test, which increased from the initial level of 34% to 44% after 5 h’ 35-step learning. Compared with a well-designed model-based predictive energy management control policy, the model-free predictive energy management method can increase the prediction horizon length by 71% (from 35 to 65 steps with 1 s interval in real-time computation) and can save energy by at least 7.8% for the same driving conditions.
Original language | English |
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Article number | 113755 |
Number of pages | 12 |
Journal | Applied Energy |
Volume | 255 |
Early online date | 28 Aug 2019 |
DOIs | |
Publication status | Published - 1 Dec 2019 |
Keywords
- Energy management
- Hybrid electric vehicle
- Markov decision problem
- Model-free predictive control
- Multi-step reinforcement learning
ASJC Scopus subject areas
- Building and Construction
- Energy(all)
- Mechanical Engineering
- Management, Monitoring, Policy and Law
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Dive into the research topics of 'Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle'. Together they form a unique fingerprint.Prizes
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China Scholarship Council Research Excellence Awards
Zhou, Quan (Recipient), 2019
Prize: Prize (including medals and awards)
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Ratcliffe Prize for the best postgraduate research in the Sciences
Zhou, Quan (Recipient), 12 Dec 2019
Prize: Prize (including medals and awards)