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http://hdl.handle.net/1942/40602
Title: | An Incremental Capacity Analysis‐based State‐of‐health Estimation Model for Lithium‐ion Batteries in High‐power Applications | Authors: | HAMED, Hamid Yusuf, Marwan Suliga, Marek GHALAMI CHOOBAR, Behnam Kostos, Ryan SAFARI, Momo |
Issue Date: | 2023 | Publisher: | WILEY-V C H VERLAG GMBH | Source: | Batteries & Supercaps, (Art N° e202300140) | Status: | Early view | Abstract: | The Incremental Capacity (IC) is a rich source of data for the state-of-health estimation of lithium-ion batteries. This data is typically collected during a low C-rate (dis)charge of the battery which is not representative of many real-world applications outside the research laboratories. Here, this limitation is showcased to be mitigated by employing a new feature-extraction technique applied to a large dataset including 105 batteries with cycle lives ranging from 158 to 1637 cycles. The state-of-health of these batteries is successfully predicted with a mean-absolute-percentage error below 0.7 % by using three regression models of support vector regressor, multi-layer perceptron, and random forest. The methodologies proposed in this work facilitate the development of accurate IC-based state-of-health predictors for lithium-ion batteries in on-board applications. | Keywords: | battery;incremental capacity;state of health | Document URI: | http://hdl.handle.net/1942/40602 | e-ISSN: | 2566-6223 | DOI: | 10.1002/batt.202300140 | ISI #: | WOS:001011873900001 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Batteries Supercaps - 2023 - Hamed - An Incremental Capacity Analysis‐based State‐of‐health Estimation Model for.pdf | Early view | 2.11 MB | Adobe PDF | View/Open |
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