Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles
The success of second-life (SL) Li-ion batteries from electric vehicles is still conditioned by their technical and economic viability. The knowledge of the internal parameters of retired batteries at the repurposing stage is key to ensure their adequate operation and to enlarge SL lifetime. However...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| 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:2454/44994 |
| Acceso en línea: | https://hdl.handle.net/2454/44994 |
| Access Level: | acceso abierto |
| Palabra clave: | Characterization Electric vehicle Energy storage Lithium-ion battery Second-life battery |
| Sumario: | The success of second-life (SL) Li-ion batteries from electric vehicles is still conditioned by their technical and economic viability. The knowledge of the internal parameters of retired batteries at the repurposing stage is key to ensure their adequate operation and to enlarge SL lifetime. However, traditional characterization methods require long testing times and specific equipment, which result in high costs that may jeopardize the economic viability of SL. In the seek of optimizing the repurposing stage, this contribution proposes a novel fast characterization method that allows to estimate capacity and internal resistance at various state of charge for reused cells, modules and battery packs. Three estimation models are proposed. The first of them is based on measurements of AC resistance, the second on DC resistance and the third combines both resistance types. These models are validated in 506 cells, 203 modules and 3 battery packs from different Nissan Leaf vehicles. The results achieved are satisfactory, with mean absolute percentage errors (MAPE) below 2.5% at cell and module level in capacity prediction and lower than 2.4% in resistance estimation. Considering battery pack level, MAPE is below 4.2% and 1.8% in capacity and resistance estimation respectively. With the proposed method, testing times are reduced from more than one day to 2 min per cell, while energy consumption is lowered from 1.4 kWh to 1 Wh. In short, this study contributes to the reduction of repurposing procedures and costs, and ultimately to the success of SL batteries business model. |
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