Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information

Qiao Zhang, Weiwen Deng, Jian Wu, Feng Ju, Jingshan Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Accurate and reliable prediction on power demand is critically important for effective power or energy management for hybrid energy storage systems with battery- super-capacitor for electric vehicles. Terrain information is one of the most common factors on power demand prediction from both driving and regenerative braking. This paper first establishes system dynamic models with battery, super-capacitor and electric motor. Based on these models, the dynamic response and characteristics of battery and super-capacitor are analyzed. Then the system time constant is formulated and studied in order to predict the dynamic power demand for battery-super-capacitor hybrid energy storage system of electric vehicle. Simulation has been conducted to verify that the proposed method in predicting dynamic power demand of electric vehicle is valid.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-87
Number of pages6
ISBN (Print)9781479958573
DOIs
StatePublished - Nov 13 2014
Externally publishedYes
Event2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014 - San Diego, United States
Duration: Nov 8 2014 → …

Other

Other2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014
CountryUnited States
CitySan Diego
Period11/8/14 → …

Fingerprint

Electric vehicles
Energy storage
Capacitors
Regenerative braking
Electric motors
Energy management
Dynamic response
Dynamic models
Supercapacitor

Keywords

  • battery
  • hybrid energy storage system
  • power demand prediction
  • supercapacitor

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Fuel Technology

Cite this

Zhang, Q., Deng, W., Wu, J., Ju, F., & Li, J. (2014). Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information. In Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014 (pp. 82-87). [6957051] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWIES.2014.6957051

Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information. / Zhang, Qiao; Deng, Weiwen; Wu, Jian; Ju, Feng; Li, Jingshan.

Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 82-87 6957051.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, Q, Deng, W, Wu, J, Ju, F & Li, J 2014, Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information. in Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014., 6957051, Institute of Electrical and Electronics Engineers Inc., pp. 82-87, 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014, San Diego, United States, 11/8/14. https://doi.org/10.1109/IWIES.2014.6957051
Zhang Q, Deng W, Wu J, Ju F, Li J. Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information. In Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 82-87. 6957051 https://doi.org/10.1109/IWIES.2014.6957051
Zhang, Qiao ; Deng, Weiwen ; Wu, Jian ; Ju, Feng ; Li, Jingshan. / Dynamic power demand prediction for battery-supercapacitor hybrid energy storage system of electric vehicle with terrain information. Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 82-87
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