Energy management analysis using customised cycles in electric/hybrid vehicles for urban environments with steep terrain [Análisis de gestión energética mediante ciclos personalizados en vehículos eléctricos/híbridos para entornos urbanos orográficos]
DOI:
https://doi.org/10.62574/rmpi.v5iTecnologia.414Keywords:
energy management, driving cycles, electric vehicles, (Source: UNESCO Thesaurus).Abstract
Efficient energy management in electric and hybrid vehicles represents a complex technological challenge that requires strategies tailored to specific local conditions. This systematic review analyses 20 scientific studies published between 2018 and 2024, following the PRISMA protocol, to examine energy management strategies based on personalised driving cycles. The results show that cycle customisation improves the accuracy of consumption estimates by 18%, while artificial intelligence techniques such as convolutional neural networks achieve errors of less than 1.5% in state of charge estimation. Energy management strategies based on Q-learning reinforcement learning reduce battery degradation by up to 20% compared to traditional methods. The integration of topographical and climatic variables is essential to optimise performance in Andean regions, where slopes greater than 4% increase energy consumption by 13.7%.
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