A new algorithm for a fast testing and sorting system applied to battery clustering

Wang, Q.; Cheng, X.J.; Wang, J.

Abstract

Battery clustering is to sort out homogeneous battery cells to form a battery pack with high uniformity, which is of great importance to prolong the cycle life of the lithium-ion battery. The traditional method for battery clustering is to compare the charge and discharge characteristic curves of the battery cells. This paper proposes a new algorithm, the squeeze algorithm, for a fast testing and sorting system to save the test time and improve efficiency. The algorithm is based on a database of complete charge and discharge characteristic curves. With a battery cell to be clustered, it will go through a short time test and get a partial curve. And then the partial curve will be compared with the database curves to get clustered. Experiments have been conducted with 100 battery cells to be clustered with 1111 database batteries. The clustering results show high accuracy, which indicates that the proposed algorithm is feasible for battery clustering.

Citation

Wang, Q., Cheng, X., & Wang, J. (2017, June). A new algorithm for a fast testing and sorting system applied to battery clustering. Presented at 6th International Conference on Clean Electrical Power - Renewable Energy Resources Impact., Santa Margherita Ligure, Liguria, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 6th International Conference on Clean Electrical Power - Renewable Energy Resources Impact.
• Start Date: Jun 27, 2017
• End Date: Jun 29, 2017
• Acceptance Date: Apr 3, 2017
• Online Publication Date: Aug 18, 2017
• Publication Date: Aug 18, 2017
• Deposit Date: Apr 18, 2017
• Publicly Available Date: Apr 18, 2017
• Pages: 397-402
• Series ISSN: 2474-9664
• Book Title: Renewable energy resources impact : 2017 6th International Conference on Clean Electrical Power (ICCEP) : Santa Margherita Ligure, 27-29 June 2017.
• ISBN: 9781509046836
• DOI: https://doi.org/10.1109/iccep.2017.8004846
• Public URL: https://durham-repository.worktribe.com/output/1147222