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

doi:10.1109/IWIES.2014.6957051

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: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 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. © 2014 IEEE.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014
Publisher	IEEE
Pages	82-87
ISBN (Print)	9781479958573
DOIs	https://doi.org/10.1109/IWIES.2014.6957051
Publication status	Published - 13 Nov 2014
Externally published	Yes
Event	2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014 - San Diego, United States Duration: 8 Nov 2014 → …

Conference

Conference	2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014
Place	United States
City	San Diego
Period	8/11/14 → …

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

The authors wish to acknowledge the support of Chinese National High-tech R&D Program (863 Plan) under grant 2012AA110904, and National Science Foundation of China under grant 51105169 and 51175215.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

battery
hybrid energy storage system
power demand prediction
supercapacitor

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10.1109/IWIES.2014.6957051

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@inproceedings{eb02b7be221e4dffac1e8dd4f28db3e4,

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

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. {\textcopyright} 2014 IEEE.",

keywords = "battery, hybrid energy storage system, power demand prediction, supercapacitor",

author = "Qiao Zhang and Weiwen Deng and Jian Wu and Feng Ju and Jingshan Li",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014 ; Conference date: 08-11-2014",

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month = nov,

day = "13",

doi = "10.1109/IWIES.2014.6957051",

language = "English",

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pages = "82--87",

booktitle = "Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014",

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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, IEEE, pp. 82-87, 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014, San Diego, United States, 8/11/14. 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 et al.
Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014. IEEE, 2014. p. 82-87 6957051.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Zhang, Qiao

AU - Deng, Weiwen

AU - Wu, Jian

AU - Ju, Feng

AU - Li, Jingshan

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2014/11/13

Y1 - 2014/11/13

N2 - 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. © 2014 IEEE.

AB - 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. © 2014 IEEE.

KW - battery

KW - hybrid energy storage system

KW - power demand prediction

KW - supercapacitor

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BT - Proceedings - 2014 IEEE International Workshop on Intelligent Energy Systems, IWIES 2014

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ER -

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

Abstract

Conference

Bibliographical note

Funding

UN SDGs

Research Keywords

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