Development of machine learning methods in hybrid energy storage systems in electric vehicles

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role i...

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Autores:
Tzu-Chia, Chen
Alazzawi, Fouad Jameel Ibrahim
Grimaldo Guerrero, John William
Chetthamrongchai, Paitoon
Dorofeev, Aleksei
Aras masood, Ismael
Ahmed, Dr. Alim Al Ayub
Akhmadeev, Ravil
Latipah, Asslia Johar
Abu Al-Rejal, Hussein
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9115
Acceso en línea:
https://hdl.handle.net/11323/9115
https://doi.org/10.1155/2022/3693263
https://repositorio.cuc.edu.co/
Palabra clave:
Machine learning
Hybrid energy
Electric vehicles
Storage systems
Rights
openAccess
License
Copyright © 2022 Tzu-Chia Chen et al.
id RCUC2_64fe8f9441a27a69a85fa9192522d7d2
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9115
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Development of machine learning methods in hybrid energy storage systems in electric vehicles
title Development of machine learning methods in hybrid energy storage systems in electric vehicles
spellingShingle Development of machine learning methods in hybrid energy storage systems in electric vehicles
Machine learning
Hybrid energy
Electric vehicles
Storage systems
title_short Development of machine learning methods in hybrid energy storage systems in electric vehicles
title_full Development of machine learning methods in hybrid energy storage systems in electric vehicles
title_fullStr Development of machine learning methods in hybrid energy storage systems in electric vehicles
title_full_unstemmed Development of machine learning methods in hybrid energy storage systems in electric vehicles
title_sort Development of machine learning methods in hybrid energy storage systems in electric vehicles
dc.creator.fl_str_mv Tzu-Chia, Chen
Alazzawi, Fouad Jameel Ibrahim
Grimaldo Guerrero, John William
Chetthamrongchai, Paitoon
Dorofeev, Aleksei
Aras masood, Ismael
Ahmed, Dr. Alim Al Ayub
Akhmadeev, Ravil
Latipah, Asslia Johar
Abu Al-Rejal, Hussein
dc.contributor.author.spa.fl_str_mv Tzu-Chia, Chen
Alazzawi, Fouad Jameel Ibrahim
Grimaldo Guerrero, John William
Chetthamrongchai, Paitoon
Dorofeev, Aleksei
Aras masood, Ismael
Ahmed, Dr. Alim Al Ayub
Akhmadeev, Ravil
Latipah, Asslia Johar
Abu Al-Rejal, Hussein
dc.subject.proposal.eng.fl_str_mv Machine learning
Hybrid energy
Electric vehicles
Storage systems
topic Machine learning
Hybrid energy
Electric vehicles
Storage systems
description The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-04-05T12:48:49Z
dc.date.available.none.fl_str_mv 2022-04-05T12:48:49Z
dc.date.issued.none.fl_str_mv 2022
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 1024-123X
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9115
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1155/2022/3693263
dc.identifier.doi.spa.fl_str_mv 10.1155/2022/3693263
dc.identifier.eissn.spa.fl_str_mv 1563-5147
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1024-123X
10.1155/2022/3693263
1563-5147
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9115
https://doi.org/10.1155/2022/3693263
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Mathematical Problems in Engineering
dc.relation.references.spa.fl_str_mv [1] F. Arslan, “A review of machine learning models for software cost estimation,” Review of Computer Engineering Research, vol. 6, no. 2, pp. 64–75, 2019.
[2] A. Manishimwe, H. Alexander, H. Kaluuma, and M. Dida, “Integrated mobile application based on machine learning for East Africa stock market,” Journal of Information Systems Engineering & Management, vol. 6, no. 3, Article ID em0143, 2021.
[3] L. H. Salazar, A. Fernandes, R. Dazzi, N. Garcia, and V. R. Leithardt, “Using different models of machine learning to predict attendance at medical appointments,” Journal of Information Systems Engineering and Management, vol. 5, no. 4, Article ID em0122, 2020.
[4] R. Gaussmann, D. Coelho, A. Fernandes, P. Crocker, and V. R. Q. Leithardt, “Estimated maintenance costs of Brazilian highways using machine learning algorithms,” Journal of Information Systems Engineering and Management, vol. 5, no. 3, Article ID em0119, 2020.
[5] Y.-P. Xu, P. Ouyang, S.-M. Xing, L.-Y. Qi, M. khayatnezhad, and H. Jafari, “Optimal structure design of a PV/FC HRES using amended Water Strider Algorithm,” Energy Reports, vol. 7, pp. 2057–2067, 2021.
[6] A. Ma, J. Ji, and M. Khayatnezhad, “Risk-constrained nonprobabilistic scheduling of coordinated power-to-gas conversion facility and natural gas storage in power and gas based energy systems,” Sustainable Energy, Grids and Networks, vol. 26, Article ID 100478, 2021.
[7] R. A. Salas-Rueda, “Analysis on the use of continuous improvement, technology and flipped classroom in the teachinglearning process by means of data science,” ,e Online Journal of Communication and Media Technologies, vol. 8, no. 4, pp. 325–343, 2018.
[8] C. D´epature, S. Jemei, L. Boulon et al., “Energy management in fuel-cell\\/battery vehicles: key issues identified in the IEEE vehicular technology society motor vehicle challenge 2017,” IEEE Vehicular Technology Magazine, vol. 13, no. 3, pp. 144–151, 2018.
[9] B. Subiyakto and S. Kot, “,e government reform on healthcare facilities from the standpoint of service quality performance,” International Journal of Economics and Finance Studies, vol. 12, no. 1, pp. 16–31, 2020.
[10] A. Mostafaeipour, M. Qolipour, H. Goudarzi et al., “Implementation of adaptive neuro-fuzzy inference system (ANFIS) for performance prediction of fuel cell parameters,” Journal of Renewable Energy and Environment, vol. 6, no. 3, pp. 7–15, 2019.
[11] L. Mapfumo and S. Mutereko, “Contextualising stakeholder participation in the governance of Harare’s informal economy sector,” International Journal of Economics and Finance Studies, vol. 12, no. 1, pp. 103–118, 2020.
[12] A. J. De Bruyn, “Harnessing Hr governance in effective virtual teams,” International Journal of Social Sciences and Humanity Studies, vol. 12, no. 1, pp. 1–17, 2020.
[13] A. Mostafaeipour, A. Goli, and M. Qolipour, “Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study,” ,e Journal of Supercomputing, vol. 74, no. 10, pp. 5461–5484, 2018.
[14] N. E. L. Danielle and L. Masilela, “Open governance for improved service delivery innovation in South Africa,” International Journal of eBusiness and eGovernment Studies, vol. 12, no. 1, pp. 33–47, 2020.
[15] K. Mosala and E. Chinomona, “Motorists attitudes towards implementation of E-tolls in Gauteng Province, South Africa,” International Journal of eBusiness and eGovernment Studies, vol. 12, no. 1, pp. 48–62, 2020.
[16] A. H. Rasouli Amirabadi and M. Mirzaei, “Photosensitization of coronene–purine hybrids for photodynamic therapy,” Quarterly Journal of Iranian Chemical Communication, vol. 7, no. 4, pp. 352–471, 2019.
[17] A. El-Khateeb, “Practical biochemistry p rinciples and techniques approach,” Progress in Chemical and Biochemical Research, vol. 3, no. 3, pp. 180–193, 2020.
[18] I. H. Sarker, “Machine learning: algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, no. 3, pp. 1–21, 2021.
[19] A. Rahimian Boogar, A. Gholamalizadeh Ahangar, and E. Shirmohamadi, “Efficiency of foliar application of humic acid on improve absorb of K than Na and salt tolerance in petunia hybrida L,” International Journal of Advanced Biological and Biomed ical Research, vol. 2, no. 42, pp. 256–259.
[20] M. Rahimi, S. Jamehbozorgi, H. Chermette, R. Ghiasi, and M. Poor Kalhor, “Computational study of substituent effect on the electronic properties of ferrocylidene acetophenones complexes,” Eurasian Chemical Communications, vol. 1, no. 5, pp. 411–418, 2019.
[21] Y. Wang, X. Wu, B. Shao, X. Yang, G. Owens, and H. Xu, “Boosting solar steam generation by structure enhanced energy management,” Science Bulletin, vol. 65, no. 16, pp. 1380–1388, 2020.
[22] W. Bi, Y. Shu, W. Dong, and Q. Yang, “Real-time energy management of microgrid using reinforcement learning,” in Proceedings of the 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pp. 38–41, IEEE, Xuzhou, China, October 2020.
[23] Z. Yang, F. Wu, and J. Zhao, “A survey: limited data problem and strategy of reinforcement learning,” in Proceedings of the 2021 Chinese Intelligent Automation Conference, Springer, Singapore, pp. 471–481, 2022.
[24] A. Chatzivasileiadi, E. Ampatzi, and I. Knight, “Characteristics of electrical energy storage technologies and their applications in buildings,” Renewable and Sustainable Energy Reviews, vol. 25, pp. 814–830, 2013.
[25] O. Z. Sharaf and M. F. Orhan, “An overview of fuel cell technology: fundamentals and applications,” Renewable and Sustainable Energy Reviews, vol. 32, pp. 810–853, 2014.
[26] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, London UK, 2018.
[27] R. C. Hsu, C. T. Liu, and D. Y. Chan, “A reinforcementlearning-based assisted power management with QoR provisioning for human–electric hybrid bicycle,” IEEE Transactions on Industrial Electronics, vol. 59, no. 8, pp. 3350–3359, 2011.
[28] X. Qi, G. Wu, K. Boriboonsomsin, M. J. Barth, and J. Gonder, “Data-driven reinforcement learning-based real-time energy management system for plug-in hybrid electric vehicles,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2572, no. 1, pp. 1–8, 2016.
[29] C. Liu and Y. L. Murphey, “Power management for plug-in hybrid electric vehicles using reinforcement learning with trip information,” in Proceedings of the 2014 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–6, IEEE, Dearborn, MI, USA, June 2014.
[30] T. Liu, Y. Zou, D. Liu, and F. Sun, “Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle,” IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7837–7846, 2015.
[31] J. Hu, D. Liu, C. Du, F. Yan, and C. Lv, “Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition,” Energy, vol. 198, p. 117298, 2020.
[32] A. A. Kamel, H. Rezk, and M. A. Abdelkareem, “Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy,” International Journal of Hydrogen Energy, vol. 46, no. 8, pp. 6061–6075, 2021.
[33] P. Zhao, Y. Wang, N. Chang, Q. Zhu, and X. Lin, “A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles,” in Proceedings of the 2018 23rd Asia and South Pacific design automation conference (ASP-DAC), pp. 196–202, IEEE, Jeju, South Korea, January 2018.
[34] R. Xiong, J. Cao, and Q. Yu, “Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle,” Applied Energy, vol. 211, pp. 538–548, 2018.
[35] H. Yu, M. Kuang, and R. McGee, “Trip-oriented energy management control strategy for plug-in hybrid electric vehicles,” in Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 1323–1336, Orlando, FL, USA, January 2013.
[36] X. Lin, Z. Wang, and J. Wu, “Energy management strategy based on velocity prediction using back propagation neural network for a plug-in fuel cell electric vehicle,” International Journal of Energy Research, vol. 45, no. 2, pp. 2629–2643, 2021.
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dc.rights.spa.fl_str_mv Copyright © 2022 Tzu-Chia Chen et al.
Atribución 4.0 Internacional (CC BY 4.0)
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Atribución 4.0 Internacional (CC BY 4.0)
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spelling Tzu-Chia, ChenAlazzawi, Fouad Jameel IbrahimGrimaldo Guerrero, John WilliamChetthamrongchai, PaitoonDorofeev, AlekseiAras masood, IsmaelAhmed, Dr. Alim Al AyubAkhmadeev, RavilLatipah, Asslia JoharAbu Al-Rejal, Hussein2022-04-05T12:48:49Z2022-04-05T12:48:49Z20221024-123Xhttps://hdl.handle.net/11323/9115https://doi.org/10.1155/2022/369326310.1155/2022/36932631563-5147Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.8 páginasapplication/pdfengHindawi Publishing CorporationUnited StatesCopyright © 2022 Tzu-Chia Chen et al.Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Development of machine learning methods in hybrid energy storage systems in electric vehiclesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.hindawi.com/journals/mpe/2022/3693263/Mathematical Problems in Engineering[1] F. Arslan, “A review of machine learning models for software cost estimation,” Review of Computer Engineering Research, vol. 6, no. 2, pp. 64–75, 2019.[2] A. Manishimwe, H. Alexander, H. Kaluuma, and M. Dida, “Integrated mobile application based on machine learning for East Africa stock market,” Journal of Information Systems Engineering & Management, vol. 6, no. 3, Article ID em0143, 2021.[3] L. H. Salazar, A. Fernandes, R. Dazzi, N. Garcia, and V. R. Leithardt, “Using different models of machine learning to predict attendance at medical appointments,” Journal of Information Systems Engineering and Management, vol. 5, no. 4, Article ID em0122, 2020.[4] R. Gaussmann, D. Coelho, A. Fernandes, P. Crocker, and V. R. Q. Leithardt, “Estimated maintenance costs of Brazilian highways using machine learning algorithms,” Journal of Information Systems Engineering and Management, vol. 5, no. 3, Article ID em0119, 2020.[5] Y.-P. Xu, P. Ouyang, S.-M. Xing, L.-Y. Qi, M. khayatnezhad, and H. Jafari, “Optimal structure design of a PV/FC HRES using amended Water Strider Algorithm,” Energy Reports, vol. 7, pp. 2057–2067, 2021.[6] A. Ma, J. Ji, and M. Khayatnezhad, “Risk-constrained nonprobabilistic scheduling of coordinated power-to-gas conversion facility and natural gas storage in power and gas based energy systems,” Sustainable Energy, Grids and Networks, vol. 26, Article ID 100478, 2021.[7] R. A. Salas-Rueda, “Analysis on the use of continuous improvement, technology and flipped classroom in the teachinglearning process by means of data science,” ,e Online Journal of Communication and Media Technologies, vol. 8, no. 4, pp. 325–343, 2018.[8] C. D´epature, S. Jemei, L. Boulon et al., “Energy management in fuel-cell\\/battery vehicles: key issues identified in the IEEE vehicular technology society motor vehicle challenge 2017,” IEEE Vehicular Technology Magazine, vol. 13, no. 3, pp. 144–151, 2018.[9] B. Subiyakto and S. Kot, “,e government reform on healthcare facilities from the standpoint of service quality performance,” International Journal of Economics and Finance Studies, vol. 12, no. 1, pp. 16–31, 2020.[10] A. Mostafaeipour, M. Qolipour, H. Goudarzi et al., “Implementation of adaptive neuro-fuzzy inference system (ANFIS) for performance prediction of fuel cell parameters,” Journal of Renewable Energy and Environment, vol. 6, no. 3, pp. 7–15, 2019.[11] L. Mapfumo and S. Mutereko, “Contextualising stakeholder participation in the governance of Harare’s informal economy sector,” International Journal of Economics and Finance Studies, vol. 12, no. 1, pp. 103–118, 2020.[12] A. J. De Bruyn, “Harnessing Hr governance in effective virtual teams,” International Journal of Social Sciences and Humanity Studies, vol. 12, no. 1, pp. 1–17, 2020.[13] A. Mostafaeipour, A. Goli, and M. Qolipour, “Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study,” ,e Journal of Supercomputing, vol. 74, no. 10, pp. 5461–5484, 2018.[14] N. E. L. Danielle and L. Masilela, “Open governance for improved service delivery innovation in South Africa,” International Journal of eBusiness and eGovernment Studies, vol. 12, no. 1, pp. 33–47, 2020.[15] K. Mosala and E. Chinomona, “Motorists attitudes towards implementation of E-tolls in Gauteng Province, South Africa,” International Journal of eBusiness and eGovernment Studies, vol. 12, no. 1, pp. 48–62, 2020.[16] A. H. Rasouli Amirabadi and M. Mirzaei, “Photosensitization of coronene–purine hybrids for photodynamic therapy,” Quarterly Journal of Iranian Chemical Communication, vol. 7, no. 4, pp. 352–471, 2019.[17] A. El-Khateeb, “Practical biochemistry p rinciples and techniques approach,” Progress in Chemical and Biochemical Research, vol. 3, no. 3, pp. 180–193, 2020.[18] I. H. Sarker, “Machine learning: algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, no. 3, pp. 1–21, 2021.[19] A. Rahimian Boogar, A. Gholamalizadeh Ahangar, and E. Shirmohamadi, “Efficiency of foliar application of humic acid on improve absorb of K than Na and salt tolerance in petunia hybrida L,” International Journal of Advanced Biological and Biomed ical Research, vol. 2, no. 42, pp. 256–259.[20] M. Rahimi, S. Jamehbozorgi, H. Chermette, R. Ghiasi, and M. Poor Kalhor, “Computational study of substituent effect on the electronic properties of ferrocylidene acetophenones complexes,” Eurasian Chemical Communications, vol. 1, no. 5, pp. 411–418, 2019.[21] Y. Wang, X. Wu, B. Shao, X. Yang, G. Owens, and H. Xu, “Boosting solar steam generation by structure enhanced energy management,” Science Bulletin, vol. 65, no. 16, pp. 1380–1388, 2020.[22] W. Bi, Y. Shu, W. Dong, and Q. Yang, “Real-time energy management of microgrid using reinforcement learning,” in Proceedings of the 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pp. 38–41, IEEE, Xuzhou, China, October 2020.[23] Z. Yang, F. Wu, and J. Zhao, “A survey: limited data problem and strategy of reinforcement learning,” in Proceedings of the 2021 Chinese Intelligent Automation Conference, Springer, Singapore, pp. 471–481, 2022.[24] A. Chatzivasileiadi, E. Ampatzi, and I. Knight, “Characteristics of electrical energy storage technologies and their applications in buildings,” Renewable and Sustainable Energy Reviews, vol. 25, pp. 814–830, 2013.[25] O. Z. Sharaf and M. F. Orhan, “An overview of fuel cell technology: fundamentals and applications,” Renewable and Sustainable Energy Reviews, vol. 32, pp. 810–853, 2014.[26] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, London UK, 2018.[27] R. C. Hsu, C. T. Liu, and D. Y. Chan, “A reinforcementlearning-based assisted power management with QoR provisioning for human–electric hybrid bicycle,” IEEE Transactions on Industrial Electronics, vol. 59, no. 8, pp. 3350–3359, 2011.[28] X. Qi, G. Wu, K. Boriboonsomsin, M. J. Barth, and J. Gonder, “Data-driven reinforcement learning-based real-time energy management system for plug-in hybrid electric vehicles,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2572, no. 1, pp. 1–8, 2016.[29] C. Liu and Y. L. Murphey, “Power management for plug-in hybrid electric vehicles using reinforcement learning with trip information,” in Proceedings of the 2014 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–6, IEEE, Dearborn, MI, USA, June 2014.[30] T. Liu, Y. Zou, D. Liu, and F. Sun, “Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle,” IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7837–7846, 2015.[31] J. Hu, D. Liu, C. Du, F. Yan, and C. Lv, “Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition,” Energy, vol. 198, p. 117298, 2020.[32] A. A. Kamel, H. Rezk, and M. A. Abdelkareem, “Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy,” International Journal of Hydrogen Energy, vol. 46, no. 8, pp. 6061–6075, 2021.[33] P. Zhao, Y. Wang, N. Chang, Q. Zhu, and X. Lin, “A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles,” in Proceedings of the 2018 23rd Asia and South Pacific design automation conference (ASP-DAC), pp. 196–202, IEEE, Jeju, South Korea, January 2018.[34] R. Xiong, J. Cao, and Q. Yu, “Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle,” Applied Energy, vol. 211, pp. 538–548, 2018.[35] H. Yu, M. Kuang, and R. McGee, “Trip-oriented energy management control strategy for plug-in hybrid electric vehicles,” in Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 1323–1336, Orlando, FL, USA, January 2013.[36] X. Lin, Z. Wang, and J. Wu, “Energy management strategy based on velocity prediction using back propagation neural network for a plug-in fuel cell electric vehicle,” International Journal of Energy Research, vol. 45, no. 2, pp. 2629–2643, 2021.812022Machine learningHybrid energyElectric vehiclesStorage systemsPublicationORIGINALDevelopment of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles.pdfDevelopment of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles.pdfapplication/pdf1847231https://repositorio.cuc.edu.co/bitstreams/0bea65c9-2f34-4555-a11e-97a3078b3424/download0b53ca6a39bede220b5e287e61d6c90cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/0962229c-e0f2-40da-8b87-7e663448d4ac/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTDevelopment of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles.pdf.txtDevelopment of Machine Learning Methods in Hybrid Energy Storage 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