A subsystem identification technique towards battery state of health monitoring under state of charge estimation errors

Previous work framed the battery State of Health (SoH) monitoring problem as an inaccessible subsystem identification problem and conceived an approach to monitor SoH via side reaction current density estimation when State of Charge (SoC) is perfectly known. In practice, however, SoC is only estimated, and even an SoC estimation error of less than 1% can significantly undermine the accuracy of the SoH estimation. In this paper, the development of a new inaccessible subsystem identification technique, called the Two Step Filter, is presented in a linear setting to estimate the SoC error and SoH variable simultaneously and hence allow for SoH monitoring even under SoC estimation errors. The potential of the Two Step Filter is demonstrated on a linearized battery model example. The result shows that the filter can successfully track the side reaction current density despite the presence of an SoC estimation error of 1%.