Experimental investigation of power available in lithium-ion batteries

• Published in: Journal of power sources Vol. 618; p. 235168
• Main Authors: Schommer, Adriano, Sciortino, Davide Domenico, Morrey, Denise, Collier, Gordana
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30-10-2024
• Subjects: ANOVA; Design of experiments; Load history; Stack pressure; State of charge; State of power; Temperature; Load history; Temperature; ANOVA; Stack pressure; State of power; State of charge; Design of experiments
• ISSN: 0378-7753
• DOI: 10.1016/j.jpowsour.2024.235168

The reliable and cost-effective operation of battery packs relies on state of power (SOP) algorithms to estimate the available power of the system. The challenges in developing these algorithms include the nonlinear behavior of batteries under high-power demands and the impact of temperature, state of charge (SOC), stack pressure and previous load history at high C-rates. This study employs analysis of variance (ANOVA) and design of experiments (DOE) to assess the impact of key factors on the power output of lithium-ion LCO pouch cells. The findings demonstrate that the effect of cell-to-cell variation on power output is more pronounced than degradation and random errors of the experiments. Further analysis shows that temperature and state of charge have a significant influence on power availability (p-value <0.05), while stack pressure does not show a significant impact within the tested ranges (20–60 kPa). Notably, the load history factor approached the significance threshold with a p-value of 0.06, highlighting its potential importance in highly dynamic load profiles at increased C-rates. This research underscores the critical factors influencing battery performance and emphasizes the necessity of meticulous statistical methods in the development of accurate power estimation methods.
•Temperature and SOC are the most influential factors on power availability.•Effects of stack pressure (20–60 kPa) were negligible.•Load history can potentially influence power availability.•Cell-to-cell variations were more significant than experimental errors.