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A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style

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Titel: A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style
Autoren: Hu, Lin, Zhang, Dongjie, Huang, Jing, Tian, Qingtao, Berecibar, Maitane, Zou, Changfu, 1987
Quelle: Applied Energy. 402
Schlagwörter: Adaptive energy management strategy, Hybrid energy storage system configuration, Driving style, Multi-criteria evaluation framework, Electric vehicles
Beschreibung: Incorporating driving style can substantially enhance the adaptability of energy management strategies in complex urban traffic, however, comprehensively and effectively evaluating different strategies remains challenging. To address this gap, this study proposes an evaluation framework based on multi-criteria decision making (MCDM), incorporating driving style characteristics for optimal adaptability of hybrid energy storage system (HESS) control strategies. Utilizing real-world urban driving data and driving styles classification, a structured indicator system is established covering system stability, battery health, efficiency, and economy. A hybrid analytic hierarchy process (AHP) and grey relational analysis (GRA) method combines expert judgment with data-driven analysis to assign indicator weights and compute comprehensive scores. Simulation comparisons show that different strategies exhibit different performance across driving scenarios, within the proposed framework, logic threshold control (LTC) and LTC with genetic algorithm (LTC-GA) perform best under conservative and standard driving styles, respectively, whereas wavelet packet transform with GA (WPT-GA) performs best under aggressive driving for its advantage in enhancing system stability through power smoothing. By comprehensively quantifying strategy adaptability, the framework provides a rigorous basis for benchmarking and for designing personalized, flexible energy management strategies.
Zugangs-URL: https://research.chalmers.se/publication/549294
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Lin%22">Hu, Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dongjie%22">Zhang, Dongjie</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Jing%22">Huang, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Tian%2C+Qingtao%22">Tian, Qingtao</searchLink><br /><searchLink fieldCode="AR" term="%22Berecibar%2C+Maitane%22">Berecibar, Maitane</searchLink><br /><searchLink fieldCode="AR" term="%22Zou%2C+Changfu%22">Zou, Changfu</searchLink>, 1987
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  Label: Source
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  Data: <i>Applied Energy</i>. 402
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+energy+management+strategy%22">Adaptive energy management strategy</searchLink><br /><searchLink fieldCode="DE" term="%22Hybrid+energy+storage+system+configuration%22">Hybrid energy storage system configuration</searchLink><br /><searchLink fieldCode="DE" term="%22Driving+style%22">Driving style</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-criteria+evaluation+framework%22">Multi-criteria evaluation framework</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Incorporating driving style can substantially enhance the adaptability of energy management strategies in complex urban traffic, however, comprehensively and effectively evaluating different strategies remains challenging. To address this gap, this study proposes an evaluation framework based on multi-criteria decision making (MCDM), incorporating driving style characteristics for optimal adaptability of hybrid energy storage system (HESS) control strategies. Utilizing real-world urban driving data and driving styles classification, a structured indicator system is established covering system stability, battery health, efficiency, and economy. A hybrid analytic hierarchy process (AHP) and grey relational analysis (GRA) method combines expert judgment with data-driven analysis to assign indicator weights and compute comprehensive scores. Simulation comparisons show that different strategies exhibit different performance across driving scenarios, within the proposed framework, logic threshold control (LTC) and LTC with genetic algorithm (LTC-GA) perform best under conservative and standard driving styles, respectively, whereas wavelet packet transform with GA (WPT-GA) performs best under aggressive driving for its advantage in enhancing system stability through power smoothing. By comprehensively quantifying strategy adaptability, the framework provides a rigorous basis for benchmarking and for designing personalized, flexible energy management strategies.
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      – Type: doi
        Value: 10.1016/j.apenergy.2025.127005
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Adaptive energy management strategy
        Type: general
      – SubjectFull: Hybrid energy storage system configuration
        Type: general
      – SubjectFull: Driving style
        Type: general
      – SubjectFull: Multi-criteria evaluation framework
        Type: general
      – SubjectFull: Electric vehicles
        Type: general
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            NameFull: Hu, Lin
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            NameFull: Zhang, Dongjie
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            NameFull: Berecibar, Maitane
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            NameFull: Zou, Changfu
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            – D: 01
              M: 01
              Type: published
              Y: 2026
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