There is no way to directly measure the state of charge (SOC) for a Lithium-ion battery. Various methods for estimating the state of charge have been proposed like extended Kalman filtering, coulomb counting and impedance spectroscopy but these are not entirely reliable [1, 2]. The models need to be recalibrated after a few cycles in order for accurately predicting the state of charge.  

The prediction of SOC of individual cells is even more important for a stack of cells in order to efficiently use the stack. Differences in the SOC of individual cells in a stack could lead to the cells being charged/discharged to different potentials, which could affect the safety of the stack or could damage the cells [2, 3, 4].  Cell balancing therefore is an important functionality of any Battery Management System (BMS) which necessitates the accurate estimation of SOC.

In this paper, we present the estimation of SOC of individual batteries in a stack using a physics based reformulated model [4], with the use of the voltage-time data while charging the cells. This kind of model adds more fidelity to the SOC estimation as the battery undergoes degradation after many cycles. Further, optimal charging profiles for the battery stacks could be suggested that would maximize the amount of charge stored while keeping the battery safe.  Reformulated models enable efficient simulation and control for online control applications.

 Acknowledgements

The work presented herein was funded in part by the Clean Energy Institute at the University of Washington, Seattle and the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under the Award Number DE-AR0000275.

References

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