State of health estimation of second-life lithium-ion batteries under real profile operation
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...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/44659 |
| Acceso en línea: | https://hdl.handle.net/2454/44659 |
| Access Level: | acceso abierto |
| Palabra clave: | Fast charge station Lithium-ion battery Residential storage Second-life batteries State of health estimation |
| Sumario: | 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. |
|---|