A Study on the Controllability of Lithium-Ion Batteries
For battery pack designers, this work provides a method to predict control effort and select cell assemblies, but it is an incremental application of existing controllability analysis to a specific domain.
This paper analyzes the controllability of lithium-ion battery cells, showing that a higher condition number of the controllability matrix correlates with increased control effort. The analysis reveals that aging equally impacts all parameters, leading to greater control effort, and the findings are used to select optimally conditioned cell assemblies from mixed new and second-life cells.
This work explores controllability and the control effort required for lithium-ion batteries. Battery packs have become a critical technology in both personal and professional applications as a means to store large amounts of energy. Management of cells in a pack becomes increasingly difficult though, with charging and discharging operations requiring more complex strategies due to parameter variations between the cells. There are numerous studies which develop effective estimation and control schemes to reduce the impact of the imbalances present in battery packs, but the receptiveness of the individual cells to these schemes is much less explored. This paper performs a nonlinear controllability analysis for experimentally parameterized cells. A connection is shown between the condition number of a battery's controllability matrix and the amount of control effort that battery will require. This reveals that if a cell's dynamics are poorly mathematically conditioned, it will require more time or higher power to control than one that is not. The controllability condition number of each cell's model is then determined both with new and aged parameters, and a sensitivity analysis shows that the cells' conditioning is equally impacted by all parameters. This offers insight into the increased control effort required for a battery as it ages and the culprit of said increase. The results of this analysis are then used to determine the best conditioned assemblies for a batch of cells with a mix of new and second-life parameters.