The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle
Abstract
A reinforcement learning-based adaptive energy management (RLAEM) is proposed for a hybrid electric tracked vehicle (HETV) in this paper. A control oriented model of the HETV is first established, in which the state-of-charge (SOC) of battery and the speed of generator are the state variables, and the engine's torque is the control variable. Subsequently, a transition probability matrix is learned from a specific driving schedule of the HETV. The proposed RLAEM decides appropriate power split between the battery and engine-generator set (EGS) to minimize the fuel consumption over different driving schedules. With the RLAEM, not only is driver's power requirement guaranteed, but also the fuel economy is improved as well. Finally, the RLAEM is compared with the stochastic dynamic programming (SDP)-based energy management for different driving schedules. The simulation results demonstrate the adaptability, optimality, and learning ability of the RLAEM and its capacity of reducing the computation time.
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Cites
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Automotive Electric Propulsion Systems With Reduced or No Permanent Magnets: An Overview
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A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles
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Output-Feedback-Based $H_{\infty}$ Control for Vehicle Suspension Systems With Control Delay
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Cited by
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
Applications of reinforcement learning in energy systems
Perera, A. T. D., & Kamalaruban, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110618 · Cited by 420
Lu, R., Hong, S. H., & Yu, M. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2909266 · Cited by 389
Lian, R., Peng, J., Wu, Y., Tan, H., & Zhang, H. (2020) · Energy · doi:10.1016/j.energy.2020.117297 · Cited by 357
Metadata
- Authors
- Liu, T., Zou, Y., Liu, D., & Sun, F.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015-12
- Volume
- 62
- Issue
- 12
- Pages
- 7837-7846
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2008791002
- Cited by
- 278
- References
- 27
- FWCI
- 12.29
- Type
- journal article
- Language
- en
- Open access
- closed
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