Linearizing battery degradation for health-aware vehicle energy management

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dc.contributor.authorLi, Shuangqi
dc.contributor.authorZhao, Pengfei
dc.contributor.authorGu, Chenghong
dc.contributor.authorHuo, Da
dc.contributor.authorLi, Jianwei
dc.contributor.authorCheng, Shuang
dc.date.accessioned2022-11-09T11:02:30Z
dc.date.available2022-11-09T11:02:30Z
dc.date.freetoread2022-11-09
dc.date.issued2023-09
dc.date.pubOnline2022-10-28
dc.description.abstractThe utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.en_UK
dc.description.journalNameIEEE Transactions on Power Systems
dc.format.extentpp. 4890-4899
dc.identifier.citationLi S, Zhao P, Gu C, et al., (2023) Linearizing battery degradation for health-aware vehicle energy management. IEEE Transactions on Power Systems, Volume 38, Issue 5, September 2023, pp. 4890-4899en_UK
dc.identifier.issn0885-8950
dc.identifier.issueNo5
dc.identifier.urihttps://doi.org/10.1109/TPWRS.2022.3217981
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18688
dc.identifier.volumeNo38
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectElectric vehicleen_UK
dc.subjectbattery energy storage systemen_UK
dc.subjectbattery agingen_UK
dc.subjectmodel-data-driven methoden_UK
dc.subjectenergy managementen_UK
dc.subjectvehicle to griden_UK
dc.titleLinearizing battery degradation for health-aware vehicle energy managementen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2022-10-24

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