"Full factorial design of experiments dataset for parallel-connected lithium-ion cells imbalanced performance investigation"

Gabriele Piombo; Simone Fasolato; Robert Heymer; Marc F Hidalgo; Mona Faraji Niri; Davide M Raimondo; James Marco; Simona Onori

doi:10.1016/j.dib.2024.110227

"Full factorial design of experiments dataset for parallel-connected lithium-ion cells imbalanced performance investigation"

Data Brief. 2024 Feb 22:53:110227. doi: 10.1016/j.dib.2024.110227. eCollection 2024 Apr.

Authors

Gabriele Piombo^{1

2}, Simone Fasolato^{1

3}, Robert Heymer², Marc F Hidalgo², Mona Faraji Niri², Davide M Raimondo³, James Marco², Simona Onori⁴

Affiliations

¹ Visiting Scholar at the Department of Energy Science and Engineering, Stanford University, USA.
² WMG, University of Warwick, Coventry, United Kingdom.
³ Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
⁴ Department of Energy Science and Engineering, Stanford University, Stanford, USA.

Abstract

This paper shares an experimental dataset of lithium-ion battery parallel-connected modules. The campaign, conducted at the Stanford Energy Control Laboratory, employs a comprehensive full factorial Design of Experiment methodology on ladder-configured parallel strings. A total of 54 test conditions were investigated under various operating temperatures, cell-to-cell interconnection resistance, cell chemistry, and aging levels. The module-level testing procedure involved Constant Current Constant Voltage (CC-CV) charging and Constant Current (CC) discharge. Beyond monitoring total module current and voltage, Hall sensors and thermocouples were employed to measure the signals from each individual cell to quantify both current and temperature distribution within each tested module configuration. Additionally, the dataset contains cell characterization data for every cell (i.e. NCA Samsung INR21700-50E and NMC LG-Chem INR21700-M50T) used in the module-level experiments. This dataset provides valuable resources for developing battery physics-based, empirical, and data-driven models at single cell and module level. Ultimately, it contributes to advance our understanding of how cell-to-cell heterogeneity propagates within the module and how that affects the overall system performance.

Keywords: Cell characterisation; Cell-to-cell parameters variation; Current and temperature imbalance; Design of experiments; Lithium-ion battery; Parallel-connected cells.