Assessment of supercapacitor performance in a hybrid energy storage system with an EMS based on the discrete wavelet transform
Assessment of supercapacitor performance in a hybrid energy storage system with an EMS based on the discrete wavelet transform
When battery and supercapacitor (SC) Energy Storage Systems (ESSs) coexist in electric vehicles, energy management is imperative to ensure efficient power distribution based on the strengths and weaknesses of each ESS.
The decoupling of highly dynamic power demands into components that match the dynamic nature of each ESS is essential. The Discrete Wavelet Transform (DWT) has been widely recommended for this purpose as part of real time energy management systems. However, due to DWT signal processing, delays in the frequency components can undermine the benefits of hybridization.
This paper analyses the contribution of the SC to alleviate the battery when the DWT is used with and without time delay compensation using future demand prediction. Four different implementation strategies for a DWT based EMS have been evaluated using different metrics to quantify energy circulation and SC assistance during acceleration and braking. Simulation results using urban and highway driving cycles, show that obtaining the SC current reference as the difference between the real time current demand and the DWT low frequency component enhances SC assistance during acceleration and braking at the expense of higher energy circulation. The complexity added by future demand prediction does not reap SC performance benefits.
Discrete wavelet transform, Electric vehicle, Energy management system, Hybrid energy storage, Long-short term memory neural network
Robayo, Miguel
929f5969-a1b1-405e-98a2-9664580b6974
Mueller, Markus
d7397558-1140-4088-95f2-90244bab3e23
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
Abusara, Mohammad
4b927b5e-0c68-45bb-8d01-4c22d94ccc7a
January 2023
Robayo, Miguel
929f5969-a1b1-405e-98a2-9664580b6974
Mueller, Markus
d7397558-1140-4088-95f2-90244bab3e23
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
Abusara, Mohammad
4b927b5e-0c68-45bb-8d01-4c22d94ccc7a
Robayo, Miguel, Mueller, Markus, Sharkh, Suleiman and Abusara, Mohammad
(2023)
Assessment of supercapacitor performance in a hybrid energy storage system with an EMS based on the discrete wavelet transform.
Journal of Energy Storage, 57, [106200].
(doi:10.1016/j.est.2022.106200).
Abstract
When battery and supercapacitor (SC) Energy Storage Systems (ESSs) coexist in electric vehicles, energy management is imperative to ensure efficient power distribution based on the strengths and weaknesses of each ESS.
The decoupling of highly dynamic power demands into components that match the dynamic nature of each ESS is essential. The Discrete Wavelet Transform (DWT) has been widely recommended for this purpose as part of real time energy management systems. However, due to DWT signal processing, delays in the frequency components can undermine the benefits of hybridization.
This paper analyses the contribution of the SC to alleviate the battery when the DWT is used with and without time delay compensation using future demand prediction. Four different implementation strategies for a DWT based EMS have been evaluated using different metrics to quantify energy circulation and SC assistance during acceleration and braking. Simulation results using urban and highway driving cycles, show that obtaining the SC current reference as the difference between the real time current demand and the DWT low frequency component enhances SC assistance during acceleration and braking at the expense of higher energy circulation. The complexity added by future demand prediction does not reap SC performance benefits.
Text
1-s2.0-S2352152X22021892-main
- Version of Record
More information
Accepted/In Press date: 19 November 2022
e-pub ahead of print date: 29 November 2022
Published date: January 2023
Additional Information:
Funding Information:
This work was supported by the Ecuadorian Secretary of Higher Education, Science, Technology and Innovation SENESCYT. Grant ARSEQ-BEC-000765-2017 .
Publisher Copyright:
© 2022 The Author(s)
Keywords:
Discrete wavelet transform, Electric vehicle, Energy management system, Hybrid energy storage, Long-short term memory neural network
Identifiers
Local EPrints ID: 473335
URI: http://eprints.soton.ac.uk/id/eprint/473335
ISSN: 2352-152X
PURE UUID: f5c83970-903d-4870-ba5b-387691b46ca5
Catalogue record
Date deposited: 16 Jan 2023 17:34
Last modified: 17 Mar 2024 02:41
Export record
Altmetrics
Contributors
Author:
Miguel Robayo
Author:
Markus Mueller
Author:
Mohammad Abusara
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
