Improving SOC accuracy using collective estimation for Lithium Ion battery cells in series

This paper presents new methods for improving state of charge (SOC) estimation accuracy for Lithium Ion battery cells connected in series. The methods benefit from the fact that the cells share a common current trajectory. These methods extend previously studied techniques for SOC estimation, like the Extended Kalman Filter. While the existing literature focuses on estimating SOC for individual cells separately, we consider the cells in a series string collectively. We show that estimation accuracy is increased for cells in series both when they are balanced and un-balanced. We validate these methods against a control case using Monte Carlo simulation.