Analysis of a Sensor Switching Approach for State Estimation with Applications to Electrochemical Power Converters and Energy Storage Systems.

Equivalent circuit models for fuel cell stacks as well as for the series connection of battery cells are characterized by multiple series-connected RC sub-networks. In practical applications, for example, when aiming at the aging detection and monitoring of the individual cells in a fuel cell stack, it is desired to estimate the individual state variables of each of these series-connected cells. The same holds true for battery management systems that are dedicated to the state of charge (resp., voltage) equalization in a series connection of multiple battery cells. However, the pure knowledge of the current through this series connection as well as the sum over all terminal voltages is insufficient to make the overall system model fully observable. To efficiently observe the state variables in each of the series-connected subsystems, we aim at avoiding the measurement of the voltages of each individual fuel (resp., battery) cell. Instead, this paper presents a systematic sensor switching strategy which allows for estimating the individual state variables of all series-connected subsystems in an Unscented Kalman Filter framework. Simulation results are presented to compare the achievable estimation accuracy with a scenario in which all cell (resp., terminal) voltages were measured simultaneously.