Articles | Volume 13, issue 1
https://doi.org/10.5194/ms-13-147-2022
https://doi.org/10.5194/ms-13-147-2022
Review article
 | 
11 Mar 2022
Review article |  | 11 Mar 2022

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Yuzheng Zhu, Xueyuan Li, Qi Liu, Songhao Li, and Yao Xu

Related subject area

Subject: Dynamics and Control | Techniques and Approaches: Mathematical Modeling and Analysis
Research on the optimal speed of vehicles passing speed bumps on the highway based on an immune algorithm
Zhiyong Yang, Ruixiang Zhang, Zihang Guo, Jieru Guo, and Yu Zhou
Mech. Sci., 15, 315–330, https://doi.org/10.5194/ms-15-315-2024,https://doi.org/10.5194/ms-15-315-2024, 2024
Short summary
Improved strategies of the Equality Set Projection (ESP) algorithm for computing polytope projection
Binbin Pei, Wenfeng Xu, and Yinghui Li
Mech. Sci., 15, 183–193, https://doi.org/10.5194/ms-15-183-2024,https://doi.org/10.5194/ms-15-183-2024, 2024
Short summary
A Lie group variational integrator in a closed-loop vector space without a multiplier
Long Bai, Lili Xia, and Xinsheng Ge
Mech. Sci., 15, 169–181, https://doi.org/10.5194/ms-15-169-2024,https://doi.org/10.5194/ms-15-169-2024, 2024
Short summary
Improved flux linkage observer for position estimation of permanent magnet synchronous linear motor
Wenbin Yu, Guolai Yang, Liqun Wang, Darui Lin, and Ahmed Al-Zahrani
Mech. Sci., 15, 99–109, https://doi.org/10.5194/ms-15-99-2024,https://doi.org/10.5194/ms-15-99-2024, 2024
Short summary
Sliding mode control of electro-hydraulic servo system based on double observers
Xiaoyu Su and Xinyu Zheng
Mech. Sci., 15, 77–85, https://doi.org/10.5194/ms-15-77-2024,https://doi.org/10.5194/ms-15-77-2024, 2024
Short summary

Cited articles

Asghar, M., Bhatti, A. I., Ahmed, Q., and Murtaza, G.: Energy Management Strategy for Atkinson Cycle Engine Based Parallel Hybrid Electric Vehicle, IEEE Access, 6, 28008–28018, https://doi.org/10.1109/ACCESS.2018.2835395, 2018. 
Ates, Y., Erdinc, O., Uzunoglu, M., and Vural, B.: Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy, Int. J. Hydrogen. Energ., 35, 774–783, https://doi.org/10.1016/j.ijhydene.2009.11.021, 2010. 
Baumann, B., Rizzoni, G., and Washington, G.: Intelligent Control of Hybrid Vehicles Using Neural Networks and Fuzzy Logic, SAE Technical Paper, SAE International, https://doi.org/10.4271/981061, 1998. 
Bertsekas, D. P.: Dynamic programming and optimal control, 1st edn., Athena Scientific, Massachusetts, USA, 2–51, 1995. 
Birge, J. R. and Louveaux, F. V.: Introduction to Stochastic Dynamic Programming, Academic Press, Berkeley, California, 2–45, 1997. 
Download
Short summary
Energy management strategies (EMSs) play an important role in hybrid electric vehicles. In this review, EMSs based on the intelligent transportation system are considered in parallel to rule-based and optimization-based EMSs. For each EMS, a comprehensive and detailed review, based on energy management methods or algorithms, summarizes the advanced research results of scholars. The principles and the advantages and disadvantages of different EMSs are expressed using figures and tables.