State of Health (SoH) estimations, and therefore accurate battery parameter information, is becoming increasingly important for battery management. The SoH is also a core feature of the European Union’s battery passport that will be rolled out next year. Currently, active diagnostics are gaining more ground in battery management research. The same applies for active balancing. Both present several advantages over their passive counterparts, but they are typically treated separately. Can these two methods be combined to use the advantages of both in order to achieve active diagnostics through active balancing?

In this work, a bidirectional cell-to-pack-to-cell balancing topology, namely a single-switched inductor topology, is used to move charge between any combination of cells or modules. This charge redistribution allows for the creation of current waveforms passing through the cells, which enables Equivalent Circuit Model (ECM) parameter estimation through an optimization routine where the Root-Mean-Square (RMS) error of the terminal voltage is minimized. This method is dubbed the Active Parameter Estimation System (APES). The State of Charge (SoC) and temperature dependencies of the ECM parameters can be taken into account within APES.

On an aged battery system, an ECM using the newly estimated ECM parameters through APES yields a 30% lower RMS voltage error compared to the case where the original, aged parameters are still being used. The error is most prominent for higher currents. Different waveforms can be used for the estimation of different ECM parameters. In this work, the cell capacity is not updated, but this feature is possible by extending the current waveform in combination with, for example, a least-squares fit. A better capacity estimate will also enable reductions in SoC deviations.

In conclusion, APES can improve ECM parameter estimates by using active diagnostics through active balancing.