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A rapid capacity evaluation of retired electric vehicle battery modules using partial discharge test

Lookup NU author(s): Dr Mohamed Ahmeid, Dr Musbahu Muhammad, Dr Simon LambertORCiD, Dr Pierrot Attidekou, Dr Zoran Milojevic



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The AuthorsAs the number of EVs hitting the roads increased, dealing with their waste such as retired LIBs become an increasingly important issue to guarantee sustainability and reduce the cost of the recycling process. This imposes establishing a practical and cost-effective gateway testing framework to sort the retired batteries based on their remaining energy capacity, and to assign them for repurposing/reusing or for recycling to extract the raw materials. Therefore, the purpose of this paper is to introduce a practical sorting method that entails the use of incremental capacity, equivalent circuit model, and manipulated coulomb counting to evaluate the full capacity of retired battery modules based on partial discharge profile. The feasibility of the proposed method is demonstrated on both truncated full discharge profile and pulse discharge profile from partially charged battery. For the investigation, 48 lithium-ion modules from retired 24 kWh Nissan Leaf battery pack are used. The experimental results show that the proposed method is capable to estimate the full capacity with a maximum error of 5%. Furthermore, a considerable reduction in the test time is achieved, with only the terminal voltage and discharge current are used, which is of great practical significance to the battery recycling industry where the cost and time are dominant.

Publication metadata

Author(s): Ahmeid M, Muhammad M, Lambert S, Attidekou PS, Milojevic Z

Publication type: Article

Publication status: Published

Journal: Journal of Energy Storage

Year: 2022

Volume: 50

Print publication date: 01/06/2022

Online publication date: 13/04/2022

Acceptance date: 28/03/2022

Date deposited: 26/04/2022

ISSN (print): 2352-152X

ISSN (electronic): 2352-1538

Publisher: Elsevier BV


DOI: 10.1016/j.est.2022.104562


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