To estimate cell State-of-charge, the authors of [1] suggest to use an adaptive extended H-infinity filter (AEHF) together with extended Thevenin equivalent circuit model.

H-infinity filter is an algorithm to estimate a dynamic system state with discrete time updates akin to Kalman Filter.

Adaptive extended H-infinity filter has shown its advantage compared with the Kalman filters considering the uncertainty in battery dynamic models and noise statistics.

The steps of H-infinity filter algorithm are matrix manipulations (as well as in Kalman Filter). H-infinity filter is more robust than Kalman Filters because it adds some more matrixes to account for past errors and uncertainty.

Results:

Both the estimation of voltage and SoC converged fast to the reference value. Within 90% depth-of-discharge range, the voltage and SoC estimation of the Li-ion battery can always match with the reference values with mean absolute error of 0.01 V and 0.49%, respectively.

Alternatives to estimating state-of-charge with an H-infinity filter:

References

[1] Digital twin for battery systems: Cloud BMS with online SoC and SoH estimation