A smart energy management system for battery-supercapacitor in electric vehicles based on the discrete wavelet transform and deep learning
A smart energy management system for battery-supercapacitor in electric vehicles based on the discrete wavelet transform and deep learning
The Discrete Wavelet Transform (DWT) is used to distribute power between the battery and supercapacitor in an electric vehicle so that the fast dynamic power demand is met by the supercapacitor and the slow dynamic is met by the battery. This results in a decline in battery ageing as the supercapacitor absorbs the high charge and discharge stress that would otherwise be imposed on the battery. However, implementing DWT introduces a time delay that increases as the level of decomposition increases. This time delay makes real time implementation difficult. This paper proposes the use of Deep Learning Recurrent Neural Networks with Long-Short Term Memory (LSTM) units to predict the power demand from raw data and compensate for the time delay so that DWT based energy management strategy can be implemented in real time. To compensate for the delay introduced by a second level DWT, the LSTM obtained a prediction root mean squared error of 3.69KW for the federal test procedure 72 (FTP72) driving cycle. Simulation results are presented to validate the design.
discrete wavelet transform, electric vehicles, hybrid energy storage system, time delay, long-short term memory
Robayo, Miguel
929f5969-a1b1-405e-98a2-9664580b6974
Abusara, Mohammad
4b927b5e-0c68-45bb-8d01-4c22d94ccc7a
Mueller, Markus
d7397558-1140-4088-95f2-90244bab3e23
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
30 July 2020
Robayo, Miguel
929f5969-a1b1-405e-98a2-9664580b6974
Abusara, Mohammad
4b927b5e-0c68-45bb-8d01-4c22d94ccc7a
Mueller, Markus
d7397558-1140-4088-95f2-90244bab3e23
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
Robayo, Miguel, Abusara, Mohammad, Mueller, Markus and Sharkh, Suleiman
(2020)
A smart energy management system for battery-supercapacitor in electric vehicles based on the discrete wavelet transform and deep learning.
2020 22nd European Conference on Power Electronics and Applications, EPE 2020 ECCE Europe.
Abstract
The Discrete Wavelet Transform (DWT) is used to distribute power between the battery and supercapacitor in an electric vehicle so that the fast dynamic power demand is met by the supercapacitor and the slow dynamic is met by the battery. This results in a decline in battery ageing as the supercapacitor absorbs the high charge and discharge stress that would otherwise be imposed on the battery. However, implementing DWT introduces a time delay that increases as the level of decomposition increases. This time delay makes real time implementation difficult. This paper proposes the use of Deep Learning Recurrent Neural Networks with Long-Short Term Memory (LSTM) units to predict the power demand from raw data and compensate for the time delay so that DWT based energy management strategy can be implemented in real time. To compensate for the delay introduced by a second level DWT, the LSTM obtained a prediction root mean squared error of 3.69KW for the federal test procedure 72 (FTP72) driving cycle. Simulation results are presented to validate the design.
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Published date: 30 July 2020
Keywords:
discrete wavelet transform, electric vehicles, hybrid energy storage system, time delay, long-short term memory
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Local EPrints ID: 448732
URI: http://eprints.soton.ac.uk/id/eprint/448732
PURE UUID: 97810b60-abd1-415c-8941-3f3f55d6efad
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Date deposited: 04 May 2021 16:38
Last modified: 17 Mar 2024 02:41
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Contributors
Author:
Miguel Robayo
Author:
Mohammad Abusara
Author:
Markus Mueller
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