AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (4.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Cost-Based Research on Energy Management Strategy of Electric Vehicles Using Hybird Energy Storage System

School of Automobile, Chang’an University, Xi’an 710064, China, and also with College of Automotive Engineering and General Aviation, Shaanxi Vocational and Technical College, Xi’an 710038, China
School of Automobile, Chang’an University, Xi’an 710064, China
College of Automobile, Shaanxi College of Communication Technology, Xi’an 710018, China
Show Author Information

Abstract

This paper uses the minimization and weighted sum of battery capacity loss and energy consumption under driving cycles as objective functions to improve the economy of Electric Vehicles (EVs) with an hybrid energy storage system composed of power batteries and ultracapacitors. Furthermore, Dynamic Programming (DP) is employed to determine the objective function values under different weight coefficients, the comprehensive cost consisting of battery aging and power consumption costs, and the relationship between the hybrid power distribution. We also evaluate the real-time fuzzy Energy Management Strategy (EMS), fuzzy control strategies, and a strategy based on DP using the World Light vehicle Test Procedure (WLTP) driving cycle and a synthesis driving cycle derived from New European Driving Cycle (NEDC), WLTP, and Urban Dynamometer Driving Schedule (UDDS) as examples. Then, the proposed strategy is compared with the fuzzy control strategy and the strategy based on DP. Compared with fuzzy energy management strategy (namely FZY-EMS), the proposed EMS reduces the battery capacity loss and system energy consumption. The results demonstrate the effectiveness of the proposed EMS in improving EV economy.

Keywords

automobile engineeringhybrid energy storageEnergy Management Strategy (EMS)fuzzy control strategy

References

[1]

B. Zhao, Q. Song, and W. Liu, Power characterization of isolated bidirectional dual-active-bridge DC-DC converter with dual-phase-shift control, IEEE Trans. Power Electron, vol. 27, no. 9, pp. 4172–4176, 2012.
Crossref Google Scholar
[2]

C. X. Song, F. Zhou, and F. Xiao, Energy management optimization of Hybrid Energy Storage System (HESS) based on dynamic programming, (in Chinese), J. Jilin Univ. (Eng. Technol. Ed.), vol. 47, no. 1, pp. 8–14, 2017.
Google Scholar
[3]

D. Z. Yao, C. J. Xie, T. Zeng, and L. Huang, Multi-Fuzzy control based energy management strategy of battery/super-capacitor hybrid energy system of electric vehicles, (in Chinese), Automot. Eng., vol. 41, no. 6, pp. 615–624&640, 2019.
Google Scholar
[4]
Z. Y. Song, The optimization and control of libattery/supercapacitor hybrid energy storage system for bus, PhD dissertation, Tsinghua University, Beijing, China, 2016.
[5]

D. Xu, H. Zhou, B. Wang, B. G. Cao, and J. L. Wang, A simplified cascading hybrid power and its control scheme for electric vehicles, (in Chinese), Automot. Eng., vol. 39, no. 12, pp. 1368–1374, 2017.
Google Scholar
[6]

Y. T. Luo, X. T. Liu, W. Q. Liu, and X. S. Ruan, Design of hybrid power system for prolonging lifespan of lithium-ion battery applied to electric vehicles, (in Chinese), J. South China Univ. Technol. (Nat. Sci. Ed.), vol. 44, no. 3, pp. 51–59, 2016.
Google Scholar
[7]

M. Ding, G. D. Lin, Z. N. Chen, Y. Q. Luo, and B. Zhao, A control strategy for hybrid energy storage systems, (in Chinese), Proc. CSEE, vol. 32, no. 7, pp. 1–6, 2012.
Google Scholar
[8]

J. J. Hu, Y. Zheng, Z. H. Hu, and J. Xiao, Parameter matching and control strategies of hybrid energy storage system for pure electric vehicle, (in Chinese), China J. Highway Transp., vol. 31, no. 3, pp. 142–150, 2018.
Google Scholar
[9]

J. Li, Y. Z. Zhu, L. Ji, and Y. J. Xu, Optimization of fuzzy control strategy for hybrid electric vehicle, (in Chinese), Automot. Eng., vol. 38, no. 1, pp. 10–14&21, 2016.
Google Scholar
[10]
G. Tan, W. Shu, Y. Guo, and M. Liu, The application of fuzzy control in brushless DC motor for pure electric vehicle, in Proc. IEEE Int. Conf. Communication Problem-Solving (ICCP), Guilin, China, 2015, pp. 560–562.
[11]

Y. Li, X. Lu, and N. C. Kar, Rule-based control strategy with novel parameters optimization using NSGA-II for power-split PHEV operation cost minimization, IEEE Trans. Veh. Technol., vol. 63, no. 7, pp. 3051–3061, 2014.
Crossref Google Scholar
[12]

Y. H. Cheng and C. M. Lai, Control strategy optimization for parallel hybrid electric vehicles using a memetic algorithm, Energies, vol. 10, no. 3, p. 305, 2017.
Crossref Google Scholar
[13]

X. S. Hu, N. Murgovski, L. M. Johannesson, and B. Egardt, Comparison of three electrochemical energy buffers applied to a hybrid bus powertrain with simultaneous optimal sizing and energy management, IEEE Trans. Intell. Transport. Syst., vol. 15, no. 3, pp. 1193–1205, 2014.
Crossref Google Scholar
[14]

F. Zhou, C. X. Song, T. W. Liang, and F. Xiao, Parameter matching of on-board hybrid energy storage system using NSGA-II algorithm, (in Chinese), J. Jilin Univ. (Eng. Technol. Ed.), vol. 47, no. 5, pp. 1336–1343, 2017.
Google Scholar
[15]

Z. Song, H. Hofmann, J. Li, X. Han, X. Zhang, and M. Ouyang, A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles, J. Power Sources, vol. 274, pp. 400–411, 2015.
Crossref Google Scholar
[16]

Z. Chen, R. Xiong, and J. Cao, Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions, Energy, vol. 96, pp. 197–208, 2016.
Crossref Google Scholar
[17]

S. B. Xie, K. K. Zhang, Q. K. Zhang, and H. R. Luo, Study on energy management strategy for parallel plug-in hybrid electric vehicles considering battery electric-thermal-depth-of-discharge, (in Chinese), Automot. Eng., vol. 43, no. 6, pp. 791–798&832, 2021.
Google Scholar
[18]

P. Zhang, F. Yan, and C. Du, A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics, Renew. Sust. Energ. Rev., vol. 48, pp. 88–104, 2015.
Crossref Google Scholar
[19]

L. Zhang, X. Hu, Z. Wang, F. Sun, J. Deng, and D. G. Dorrell, Multiobjective optimal sizing of hybrid energy storage system for electric vehicles, IEEE Trans. Veh. Technol., vol. 67, no. 2, pp. 1027–1035, 2018.
Crossref Google Scholar
[20]

X. Wu, X. Yan, Y. Wang, B. X. Huang, and Z. C. Liu, Study on DC resistance characteristics of ternary lithium batteries, (in Chinese), Chin. J. Power Sources, vol. 43, no. 4, pp. 568–570&684, 2019.
Google Scholar
[21]

L. Zhang, X. Hu, Z. Wang, J. Ruan, C. Ma, Z. Song, D. G. Dorrell, and M. G. Pecht, Hybrid electrochemical energy storage systems: An overview for smart grid and electrified vehicle applications, Renew. Sust. Energ. Rev., vol. 139, p. 110581, 2021.
Crossref Google Scholar
[22]

Q. Wang, Z. Wang, L. Zhang, P. Liu, and L. Zhou, A battery capacity estimation framework combining hybrid deep neural network and regional capacity calculation based on real-world operating data, IEEE Trans. Ind. Electron., vol. 70, no. 8, pp. 8499–8508, 2023.
Crossref Google Scholar
[23]

D. T. Qin, Z. Y. Peng, Y. G. Liu, Z. H. Duan, and Y. Yang, Dynamic energy management strategy of HEV based on driving pattern recognition, (in Chinese), China Mechan. Eng., no. 11, pp. 1550–1555, 2014.
Google Scholar
[24]

M. Sharafi, and T. Y. El Mekkawy, Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach, Renew. Energ., vol. 68, pp. 67–79, 2014.
Crossref Google Scholar
[25]

L. H. Xi, X. Zhang, C. Geng, and Q. C. Xue, Energy management strategy optimization of extended-range electric vehicle based on dynamic programming, (in Chinese), J. Traffic Transp. Eng., vol. 18, no. 3, pp. 148–156, 2018.
Google Scholar
[26]

S. B. Xie, T. Liu, H. L. Li, and Z. K. Xin, A study on predictive energy management strategy for a plug-in hybrid electric bus based on Markov Chain, (in Chinese), Automot. Eng., vol. 40, no. 8, pp. 871–877&911, 2018.
Google Scholar
Tsinghua Science and Technology
Volume 29 Issue 3,
June 2024
Pages 684-697
DOI: 10.26599/TST.2023.9010054
Cite this article:
Zhou J, Zhao J, Wang L. Cost-Based Research on Energy Management Strategy of Electric Vehicles Using Hybird Energy Storage System. Tsinghua Science and Technology, 2024, 29(3): 684-697. https://doi.org/10.26599/TST.2023.9010054

264

Views

45

Downloads

3

Crossref

0

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 25 November 2022
Revised: 24 March 2023
Accepted: 31 May 2023
Published: 04 December 2023
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return