Redefining urban mobility: Real-world regenerative braking optimization via bio-inspired AI for electric buses energy efficiency

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dc.contributor.authorEkici, Yunus Emre
dc.contributor.authorKaradag, Teoman
dc.contributor.authorAkdag, Ozan
dc.date.accessioned2026-06-19T06:39:59Z
dc.date.available2026-06-19T06:39:59Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractIn the era of global decarbonization and sustainable urban mobility, optimizing energy recovery in electric public transport systems has become a strategic imperative. This study presents a comprehensive and data-driven investigation into the optimization of regenerative braking (RB) performance in hybrid electric trolleybuses operating in T & uuml;rkiye. Unlike conventional research relying on controlled driving cycles or laboratory simulations, this work employs real-world operational data totaling over 79 million records collected over five years to construct a high-fidelity predictive model. Using a novel meta-heuristic algorithm inspired by the physiological mechanism of water uptake and transport in plants (WUTP), eight critical parameters influencing RB power including vehicle speed, gradient, acceleration, passenger mass, ambient temperature, and auxiliary system loads were integrated into a robust mathematical framework. The resulting model, WUTP-EBREM, accurately predicts regenerative braking power under varying operational conditions, achieving a minimal error rate (RMSE: 0.12 %). This enables a fine-grained understanding of how instantaneous operating dynamics affect energy recovery in electric buses. Subsequently, the model was applied to 50 of the city's busiest bus routes, each with distinct topographical and operational characteristics. Route-based analyses revealed substantial variability in RB potential, highlighting that optimal energy recovery depends not only on vehicle design but also on contextual factors such as slope patterns and thermal loads. The findings offer direct implications for fleet energy planning, battery sizing, and route optimization, providing actionable insights for transit operators and electric vehicle manufacturers alike. Beyond its empirical contributions, this research introduces a scalable modeling architecture adaptable to various geographic regions and climate conditions, bridging the gap between theoretical energy models and field-level implementation. By capturing the stochastic, non-linear nature of urban electric bus operation, the study sets a precedent for integrating artificial intelligence into real-time transport energy optimization. The WUTP-EBREM model stands as a unique decision-support tool for smart transportation planning, offering both academic value and immediate practical utility. This work is poised to inform future studies in energy-aware vehicle control systems, sustainable transit infrastructure, and intelligent fleet management strategies in electrified urban environments.
dc.identifier.doi10.1016/j.energy.2025.138854
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.orcid0000-0001-7791-0473
dc.identifier.orcid0000-0002-7682-7771
dc.identifier.scopus2-s2.0-105018671591
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.energy.2025.138854
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5900
dc.identifier.volume338
dc.identifier.wosWOS:001600055700013
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEnergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectElectric Buses
dc.subjectRegenerative Braking Systems
dc.subjectBig Data In Transportation
dc.subjectTransport Energy Efficiency
dc.subjectSustainable Urban Mobility
dc.subjectArtificial Intelligence In Transit Systems
dc.titleRedefining urban mobility: Real-world regenerative braking optimization via bio-inspired AI for electric buses energy efficiency
dc.typeArticle

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