A data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.contributor.authorMa, Zheng
dc.contributor.authorLuan, Yixuan
dc.contributor.authorZhang, Fengqi
dc.contributor.authorXie, Shaobo
dc.contributor.authorCoskun, Serdar
dc.date.accessioned2025-03-17T12:27:17Z
dc.date.available2025-03-17T12:27:17Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractConventional energy management strategies (EMSs) for hybrid electric vehicles are devised assuming the vehicle mass remains constant under dynamic driving conditions. However, the EMSs cannot adapt to different load conditions due to the dynamic change of vehicle mass. Investigating the characteristics of vehicle mass change plays significant roles in energy optimization, thereby improving overall efficiency and mobility. To this end, we propose an adaptive EMS for a plug-in hybrid electric bus (PHEB) based on artificial neutral network-Pontryagin minimum principle (ANN-PMP) by considering mass distribution characteristics. Firstly, the skew-normal distribution characteristics of PHEB mass are analyzed, and the distribution characteristic spectrum of vehicle mass is obtained based on the Monte Carlo method. Secondly, the influence of mass uncertainty on the PMP is analyzed, and then the ANN-PMP is devised by updating a dynamic co-state with ANN. Finally, an enhanced ANN-PMP (so-called ANN-PMP-CS) is proposed by combining the ANN-PMP strategy obtained by mass distri-bution training and CS (charge sustaining strategy) to include the no-load or full-load cases. The simulation results demonstrate that the ANN-PMP can adapt to mass change while ensuring that the final state-of-charge (SOC) convergence to the target value. We also observe that the fuel economy of ANN-PMP-CS is similar to that of the widely-used dynamic programming (DP) strategy. Compared with the charge depletion-charge sus-taining (CD-CS) strategy, the fuel economy can be improved by about 46.93 % on average.
dc.description.sponsorshipNational Natural Science Foundation of China [52072047, 51905419]; Fundamental Research Funds for the Central Universities, CHD [300102221202]
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China (Grant No. 52072047, Grant No. 51905419) and Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102221202) . The authors greatly appreciate constructive discussions with Mr. Zhang Mengyang and useful comments from two anonymous reviewers.
dc.identifier.doi10.1016/j.est.2023.109963
dc.identifier.issn2352-152X
dc.identifier.issn2352-1538
dc.identifier.scopus2-s2.0-85179586704
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.est.2023.109963
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2178
dc.identifier.volume77
dc.identifier.wosWOS:001141315500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Energy Storage
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectPlug-in hybrid electric bus
dc.subjectMass uncertainty
dc.subjectEnergy management
dc.subjectArtificial neural network
dc.titleA data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty
dc.typeArticle

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