计及老化路径的锂离子电池加速寿命工况自动生成方法

doi:10.19595/j.cnki.1000-6753.tces.210336

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Abstract The lithium-ion battery (LiB) requires an aging test to ensure its reliability during long-term use. The conventional aging test is very time-consuming because the current LiB life span is thousands of cycles. Therefore, an accelerated aging test method for LiB is needed. The degradation of the LiB is path-dependent. Therefore, the constraints of designing this accelerated aging test method are: to ensure both the equivalence of LiB capacity and the invariance of its internal degradation mechanism. This paper proposes a model-based method for generating an accelerated aging profile. The objective function considering the acceleration factor and the relative error of the aging path is constructed, and the optimal solution of the objective function is obtained by a double closed-loop architecture. In the double closed-loop architecture, the outer loop is an acceleration factor optimization algorithm that aims to find the optimal acceleration factor; the inner loop is an acceleration profile search algorithm, aiming to search the optimal acceleration profile under a given acceleration factor. The accelerated aging profile of the China light-duty vehicle test cycle-passenger car (CLTC-P) is generated under the given objective function based on the proposed method. The simulation is carried out based on the semi-empirical fractional-order model considering the dominant degradation mechanism of the LiB developed by the authors, which verifies the effectiveness of the method.

Key words： Lithium-ion battery aging path double closed-loop architecture acceleration factor tabu search

Received: 12 March 2021

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TM912

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	Guo Dongxu
	Yang Geng
	Feng Xuning
	Lu Languang
	Ouyang Minggao

Cite this article:

Guo Dongxu,Yang Geng,Feng Xuning等. Accelerated Aging Profile Generation Method for Lithium-Ion Batteries Considering Aging Path[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4788-4797.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210336 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I18/4788

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