A Dataset for Large Prismatic Lithium-Ion Battery Cells (CALB L148N58A): Comprehensive Characterization and Real-World Driving Cycles

This dataset presents the experimental campaign 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.

本数据集呈现了一组包含十一块CALB L148N58A B级锂离子棱柱形电池单元的实验研究，该电池单元标称容量为58Ah。实验研究在意大利特里este大学的跨学科储能应用能源实验室（ELISA）进行，采用无损测试方法以评估该批次中每个电池单元的性能。电池单元级测试流程包括固定恒流恒压（CCCV）充电和低电流率的恒流（CC）放电，以及在不同电流速率（例如1C和C/3）下的混合脉冲功率表征（HPPC）测试、不同荷电状态（SOC）水平下的电化学阻抗谱（EIS）测试，以及三种不同的驾驶循环（WLTP、UDDS和US06）。所有实验均在三个不同的环境温度（10°C、25°C和40°C）下进行，从而得出了一个全面的电池单元性能评估数据集。本数据集为评估新电池批次中电池单元在容量和阻抗等性能指标方面的批内细胞间变化提供了宝贵的见解。此外，它还作为开发电池模型的关键资源，包括基于物理、经验性和数据驱动的方法。此外，它可能有助于验证电池管理系统中的基于模型和数据驱动的估计和控制策略，从而提高储能解决方案的可靠性和效率。