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Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (EKS)
Amity University.
University of Duisburg-Essen.
University of Duisburg-Essen.
2019 (English)In: Proceedings of the Annual Conference of the PHM Society 2019, PHM Society , 2019, Vol. 11, p. 1-8, article id 784Conference paper, Published paper (Refereed)
Abstract [en]

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimal power flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to battery life, it becomes unavoidable to develop battery lifetime models, which not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetime-oriented battery models have to be integrated into power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source was developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance with current operating conditions. Here, the change in charge / discharge rate (C-rate), overcharging / undercharging of the battery (depth-of-discharge), and the temperature are taken into consideration to define machine model states. The End-of-Lifetime (EoL) is defined as the deviation between nominal and current ampere-hour (Ah) throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serves as a prerequisite for its integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.

Place, publisher, year, edition, pages
PHM Society , 2019. Vol. 11, p. 1-8, article id 784
Keywords [en]
hybrid electric vehicles, electric vehicles, power management
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-37567DOI: 10.36001/phmconf.2019.v11i1.784ISBN: 978-1-936263-29-5 (print)OAI: oai:DiVA.org:miun-37567DiVA, id: diva2:1364845
Conference
Annual Conference of the Prognostics and Health Management Society 2019
Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-30Bibliographically approved

Open Access in DiVA

PHM784_OpenAccess(1806 kB)4 downloads
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Type fulltextMimetype application/pdf

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Publisher's full texthttp://phmpapers.org/index.php/phmconf/article/view/784

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CiteExportLink to record
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