State of health estimation of second-life lithium-ion batteries under real profile operation
| Autor: | Elisa Braco, Daniel-Ioan Stroe, Idoia San Martín, Pablo Sanchis, Alfredo Ursúa |
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| Přispěvatelé: | Universidad Pública de Navarra. Departamento de Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities, Nafarroako Unibertsitate Publikoa. Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko, Elektroniko eta Telekomunikazio Saila Saila, Gobierno de Navarra / Nafarroako Gobernua, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa |
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| Zdroj: | Aalborg University Braco, E, San Martin, I, Sanchis, P, Ursua, A & Stroe, D-I 2022, ' State of health estimation of second-life lithium-ion batteries under real profile operation ', Applied Energy, vol. 326, 119992 . https://doi.org/10.1016/j.apenergy.2022.119992 |
| DOI: | 10.1016/j.apenergy.2022.119992 |
| Popis: | The economic viability of second-life (SL) Li-ion batteries from electric vehicles (EVs) is still uncertain nowadays. Assessing the internal state of reused cells is key not only at the repurposing stage but also during their SL operation. As an alternative of the traditional capacity tests used to this end, the estimation of State of Health (SOH) allows to reduce the testing time and the need of equipment, thereby reinforcing the economic success of SL batteries. However, the estimation of SOH in real SL operation has been rarely analysed in literature. This contribution aims thus to cover this gap, by focusing on the experimental assessment of SOH estimation in reused modules from Nissan Leaf EVs under two SL scenarios: a residential household with self-consumption and a fast charge station for EVs. By means of partial charge and experimental data from cycling and calendar ageing tests, accuracy and robustness of health indicators is firstly assessed. Then, SOH estimation is carried out using real profiles, covering a SOH range from 91.3 to 31%. Offline assessment led to RMSE values of 0.6% in the residential profile and 0.8% in the fast charge station, with a reduction in testing times of 85% compared to a full capacity test. In order to avoid the interruption of battery operation, online assessment in profiles was also analysed, obtaining RMSE values below 1.3% and 3.6% in the residential and charging station scenarios, respectively. Therefore, the feasibility of SOH estimation in SL profiles is highlighted, as it allows to get accurate results reducing testing times or even without interrupting normal operation. This work is part of the projects PID2019-111262RB-I00, funded by MCIN/AEI, Spain/ 10.13039/501100011033/, STARDUST (774094), funded by European Union’s Horizon 2020 research and innovation programme, HYBPLANT (0011-1411-2022-000039), funded by Government of Navarre, Spain, and a Ph.D. scholarship, also funded by Government of Navarre. Open access funding provided by Universidad Pública de Navarra, Spain . |
| Databáze: | OpenAIRE |
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