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Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems.

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Title: Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems.
Authors: Abdelhedi, Fatma1 (AUTHOR) fabdelhedi@kau.edu.sa, Jarraya, Imen2 (AUTHOR) ijarraya@pse.edu.sa, Bawayan, Haneen1 (AUTHOR) hbawayan@kau.edu.sa, Abdelkeder, Mohamed2 (AUTHOR) mabdelkader@psu.edu.sa, Rizoug, Nassim3 (AUTHOR) nassim.rizoug@estaca.fr, Koubaa, Anis2 (AUTHOR) akoubaa@psu.edu.sa
Source: Energy. Dec2024, Vol. 313, pN.PAG-N.PAG. 1p.
Subjects: Fuzzy logic, Power density, Energy storage, Energy density, Machine learning, Fuel cells, Electric vehicle batteries, Fuel cell vehicles
Abstract: The current energy storage solutions for electric vehicles (EVs), powered by a single source such as batteries, fuel cells, flywheels, or supercapacitors (SCs), hinder efforts to make EVs comparable to conventional vehicles due to limitations in energy density, power density, and cycle life inherent in each source technology. The hybridization of different energy sources represents a significant advancement in addressing the efficiency and reliability challenges of EVs, potentially surpassing single-source systems. However, this hybridization adds complexity to managing diverse energy sources within the power management system (PMS). Our study introduces a dual-energy storage solution for EVs, combining a lithium-ion battery as the primary power source with a SC as the auxiliary energy reserve. We conduct a comparative analysis of several PMS methods, including rule-based approaches (filtering, limitation, dynamic battery power limiting (DBPL) and fuzzy logic) and neural network (NN). Through detailed simulations and experimental implementation using dSPACE 1140 and MATLAB, we assess these methods by evaluating battery stress reduction, cost-effectiveness, computational requirements, and the SC charging level at the driving cycle end. Obtained results show that some basic rule-based methods can degrade battery life even with the addition of SCs, compared to EVs with a single battery source. Enhanced rule-based methods such as Fuzzy Logic Control (FLC) and DBPL show significant potential, comparable to machine learning (ML) techniques. The conducted comparative analysis reveals that DBPL and NN methods are crucial for extending Li-ion battery life, maintaining optimal SC charging, and maximizing braking energy recovery. [ABSTRACT FROM AUTHOR]
Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems.
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  Data: <searchLink fieldCode="AR" term="%22Abdelhedi%2C+Fatma%22">Abdelhedi, Fatma</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fabdelhedi@kau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Jarraya%2C+Imen%22">Jarraya, Imen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ijarraya@pse.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Bawayan%2C+Haneen%22">Bawayan, Haneen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hbawayan@kau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Abdelkeder%2C+Mohamed%22">Abdelkeder, Mohamed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mabdelkader@psu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Rizoug%2C+Nassim%22">Rizoug, Nassim</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> nassim.rizoug@estaca.fr</i><br /><searchLink fieldCode="AR" term="%22Koubaa%2C+Anis%22">Koubaa, Anis</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> akoubaa@psu.edu.sa</i>
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Dec2024, Vol. 313, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Power+density%22">Power density</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+density%22">Energy density</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Fuel+cells%22">Fuel cells</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+vehicle+batteries%22">Electric vehicle batteries</searchLink><br /><searchLink fieldCode="DE" term="%22Fuel+cell+vehicles%22">Fuel cell vehicles</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The current energy storage solutions for electric vehicles (EVs), powered by a single source such as batteries, fuel cells, flywheels, or supercapacitors (SCs), hinder efforts to make EVs comparable to conventional vehicles due to limitations in energy density, power density, and cycle life inherent in each source technology. The hybridization of different energy sources represents a significant advancement in addressing the efficiency and reliability challenges of EVs, potentially surpassing single-source systems. However, this hybridization adds complexity to managing diverse energy sources within the power management system (PMS). Our study introduces a dual-energy storage solution for EVs, combining a lithium-ion battery as the primary power source with a SC as the auxiliary energy reserve. We conduct a comparative analysis of several PMS methods, including rule-based approaches (filtering, limitation, dynamic battery power limiting (DBPL) and fuzzy logic) and neural network (NN). Through detailed simulations and experimental implementation using dSPACE 1140 and MATLAB, we assess these methods by evaluating battery stress reduction, cost-effectiveness, computational requirements, and the SC charging level at the driving cycle end. Obtained results show that some basic rule-based methods can degrade battery life even with the addition of SCs, compared to EVs with a single battery source. Enhanced rule-based methods such as Fuzzy Logic Control (FLC) and DBPL show significant potential, comparable to machine learning (ML) techniques. The conducted comparative analysis reveals that DBPL and NN methods are crucial for extending Li-ion battery life, maintaining optimal SC charging, and maximizing braking energy recovery. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181726595
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.energy.2024.133979
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Fuzzy logic
        Type: general
      – SubjectFull: Power density
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Energy density
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Fuel cells
        Type: general
      – SubjectFull: Electric vehicle batteries
        Type: general
      – SubjectFull: Fuel cell vehicles
        Type: general
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      – TitleFull: Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems.
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              Text: Dec2024
              Type: published
              Y: 2024
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