A dataset for large prismatic lithium-ion battery cells (CALB L148N58A): Comprehensive characterization and real-world driving cyclesCALB L148N58A testing campaign
This paper presents an experimental dataset for a batch of eleven prismatic CALB L148N58A lithium-ion B-grade battery cells with a nominal capacity of 58 Ah. The experimental campaign, conducted at the Energy Laboratory for Interdisciplinary Storage Applications (ELISA) at the University of Trieste,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000332 |
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| Summary: | This paper presents an experimental dataset for a batch of eleven prismatic CALB L148N58A lithium-ion B-grade battery cells with a nominal capacity of 58 Ah. The experimental campaign, conducted at the Energy Laboratory for Interdisciplinary Storage Applications (ELISA) at the University of Trieste, Italy, employs non-destructive tests to assess the performance of each cell within the batch. The cell-level testing procedures include fixed Constant Current Constant Voltage (CCCV) charging and Constant Current (CC) discharging at low current rates, Hybrid Pulse Power Characterization (HPPC) tests at various C-rates (i.e., 1C and C/3), Electrochemical Impedance Spectroscopy (EIS) at different State of Charge (SOC) levels, and three distinct driving cycles (WLTP, UDDS and US06). All the experiments were conducted at three different ambient temperatures (10°C, 25°C, and 40°C), resulting in a comprehensive dataset for assessing the performance metrics of the battery cells. This dataset provides valuable insights into post-manufacturing cell-to-cell variations in performance metrics such as capacity and impedance within a batch of fresh cells. Additionally, it serves as a crucial resource for developing battery models, including physics-based, empirical, and data-driven approaches. Moreover, it may contribute to validate model-based and data-driven estimation and control strategies within battery management systems, enhancing the reliability and efficiency of energy storage solutions. |
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| ISSN: | 2352-3409 |