Online state of health estimation of lithium-ion batteries through subspace system identification methods

Abstract

When Lithium-ion Batteries (LiBs) reach the end of their first life in electric vehicles (EVs), they can still be used in applications with lower power demands, a process known as second-life. However, to ensure that LiBs – or cells – removed from EVs operate safely, efficiently and reliably in a second application, several tests and procedures must be applied to study their internal conditions. Naturally, one of the most important parameters to be determined is the state of health (SoH). However, the available processes for determining the SoH of lithium-ion cells are limited by high costs, relatively long test times and the need for specific equipment, limiting the second-life market. Hence, this work proposes a methodology to estimate the SoH of lithium-ion cells, based on subspace system identification (SSI) methods, where the parameters estimated for the equivalent circuit model (ECM) of a given cell are associated with its SoH. To validate the proposed methodology, nine cell samples from the same manufacturer were considered, which were removed from heavy-duty EVs at the end of their first life. The obtained results showed that: (a) good approximations between the identified models and the actual cells were achieved, with root mean square error (RMSE) values as small as 1.32 mV; (b) SSI methods can be applied online, while the LiBs are still operating in the EV during their first life, eliminating the need of additional tests; and (c) there is a clear association between ECM parameters and the SoH, so it was possible to estimate the SoH of the samples with RMSE values varying from 2.11% to 3.34%. Therefore, the proposed methodology offers significant improvements when compared to the conventional capacity tests, including the possibility of estimating the SoH relatively fast, online and without the need for specific equipment.