This paper investigates the observability of one of the most commonly usedequivalent circuit models (ECMs) for lithium-ion batteries and presents amethod to estimate the state of charge (SOC) in the presence of sensor biases,highlighting the importance of observability analysis for choosing appropriatestate estimation algorithms. Using a differential geometric approach, necessaryand sufficient conditions for the nonlinear ECM to be observable are derivedand are shown to be different from the conditions for the observability of thelinearised model. It is then demonstrated that biases in the measurements, dueto sensor ageing or calibration errors, can be estimated by applying anonlinear Kalman filter to an augmented model where the biases are incorporatedinto the state vector. Experiments are carried out on a lithium-ion pouch celland three types of nonlinear filters, the first-order extended Kalman filter(EKF), the second-order EKF and the unscented Kalman filter (UKF) are appliedusing experimental data. The different performances of the filters areexplained from the point of view of observability.
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