Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution-Reference-Cited by-同舟云学术

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Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

Published:2022-03-28 Issue:2 Volume:20 Page:145-160
ISSN:2095-6983
Container-title:Control Theory and Technology
language:en
Short-container-title:Control Theory Technol.

Author:

Xu Fuguo,Tsunogawa Hiroki,Kako Junichi,Hu Xiaosong,Li Shengbo Eben,Shen Tielong,Eriksson Lars,Guardiola Carlos

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11768-022-00086-y.pdf

Reference37 articles.

1. Zhou, Q., Zhao, D., Shuai, B., Li, Y., Williams, H., & Xu, H. (2021). Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5298–5308. https://doi.org/10.1109/TNNLS.2021.3093429

2. Xu, F., & Shen, T. (2021). Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3054903

3. Zhuang, W., Li, S., Zhang, X., et al. (2020). A survey of powertrain configuration studies on hybrid electric vehicles. Applied Energy, 262, 114553.

4. Wang, S., Chen, K., Zhao, F., & Hao, H. (2019). Technology pathways for complying with corporate average fuel consumption regulations up to 2030: A case study of China. Applied Energy, 241, 257–277.

5. Zhang, J., Shen, T., & Kako, J. (2020). Short-term optimal energy management of power-split hybrid electric vehicles under velocity tracking control. IEEE Transactions on Vehicular Technology, 69(1), 182–193.

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