Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases

This brief investigates the observability of one of the most commonly used equivalent circuit models (ECMs) for lithium-ion batteries and presents a method to estimate the state of charge in the presence of sensor biases, highlighting the importance of observability analysis for choosing appropriate state estimation algorithms. Using a differential geometric approach, necessary and sufficient conditions for the nonlinear ECM to be observable are derived and are shown to be different from the conditions for the observability of the linearized model. It is then demonstrated that biases in the measurements, due to sensor aging or calibration errors, can be estimated by applying a nonlinear Kalman filter to an augmented model where the biases are incorporated into the state vector. Experiments are carried out on a lithium-ion pouch cell and three types of nonlinear filters, the first-order extended Kalman filter (EKF), the second-order EKF, and the unscented Kalman filter, are applied using experimental data. The different performances of the filters are explained from the point of view of observability.