Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

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dc.contributor.authorXie, Shaobo
dc.contributor.authorHu, Xiaosong
dc.contributor.authorQi, Shanwei
dc.contributor.authorTang, Xiaolin
dc.contributor.authorLang, Kun
dc.contributor.authorXin, Zongke
dc.contributor.authorBrighton, James
dc.date.accessioned2022-10-26T15:52:37Z
dc.date.available2022-10-26T15:52:37Z
dc.date.issued2019-02-22
dc.description.abstractWhen developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy.en_UK
dc.identifier.citationXie S, Hu X, Qi S, et al., (2019) Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge. Energy, Volume 173, April 2019, pp. 667-678en_UK
dc.identifier.issn0360-5442
dc.identifier.urihttps://doi.org/10.1016/j.energy.2019.02.074
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18618
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEnergy managementen_UK
dc.subjectModel predictive controlen_UK
dc.subjectBattery agingen_UK
dc.subjectPlug-in hybrid electric vehicleen_UK
dc.subjectPontryagin's minimum principleen_UK
dc.titleModel predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of dischargeen_UK
dc.typeArticleen_UK

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