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Adaptive energy management strategy for Extended Range Electric Vehicles under complex road conditions based on RF-IGWO and MGO algorithms

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  • He, Liange
  • Mo, Haijun
  • Zhang, Yan
  • Wu, Limin
  • Tang, Jinwang

Abstract

This study aims to improve the adaptability of energy management strategies for Extended Range Electric Vehicles (EREVs) under complex real-world driving conditions, improving fuel economy and extending battery life. A two-stage methodology that integrates real-world road condition identification and adaptive energy management is proposed. First, a driving cycle identification model combining Random Forest (RF) and Improved Grey Wolf Optimizer (IGWO) algorithms is developed for complex road environments. And an EREV system model validated through WLTC bench tests and road experiments supports the proposed Moss Growth Optimization (MGO) based energy management strategy, which dynamically optimizes equivalent fuel consumption and battery workload through cumulative absolute ampere-hour regulation. Simulation results demonstrate 5.04 % reduction in equivalent fuel consumption, 5.86 % decrease in battery cumulative absolute ampere-hours, and 14.87 % lower generator energy loss compared to original approaches. As expected, fuel consumption is improved and battery life is extended, a result that can be used to improve the applicability of energy management on complex roads.

Suggested Citation

  • He, Liange & Mo, Haijun & Zhang, Yan & Wu, Limin & Tang, Jinwang, 2025. "Adaptive energy management strategy for Extended Range Electric Vehicles under complex road conditions based on RF-IGWO and MGO algorithms," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021425
    DOI: 10.1016/j.energy.2025.136500
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