用于电动汽车聚合充电的新型动态锂离子电池模型

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Ahmed M. Asim, Osama A. Ahmed, Amr M. Ibrahim, W. El-Khattam, Hossam E. Talaat
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引用次数: 0

摘要

为电动汽车实施成功的聚合充电策略以参与批发市场,需要一个准确的电池模型,该模型可以大规模运行,同时捕捉关键的电池动态。现有模型要么缺乏精度,要么对舰队级协调构成计算挑战。据我们所知,大多数文献广泛采用的电池模型忽略了关键的电池极化动力学,倾向于可扩展性而不是准确性,称为恒定功率模型(cpm)。因此,本文提出了一种新的线性电池模型(LBM),专门用于聚合充电策略。LBM通过线性表示来考虑电池动力学,在保持可扩展性的同时解决了现有模型的局限性。对电动汽车中常用的四种锂离子化学物质:磷酸铁锂(LFP)、镍锰钴(NMC)、锂锰氧化物(LMO)和镍钴铝(NCA)进行了模型动力学行为评估。结果表明,LBM与高保真的Thevenin等效电路模型(Th-ECM)非常接近,并且在较高的充电速率下精度显著提高。最后,以能源批发市场的竞价为例,验证了该模型的规模化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Dynamic Li-Ion Battery Model for the Aggregated Charging of EVs
Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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