Observability analysis and state estimation of lithium-ion batteries in the presence of sensor biases
For battery management system designers, this work provides a theoretical framework to choose appropriate state estimation algorithms when sensor biases are present.
This paper derives observability conditions for a lithium-ion battery equivalent circuit model and shows that sensor biases can be estimated using a nonlinear Kalman filter on an augmented model. Experimental results demonstrate that the unscented Kalman filter outperforms first- and second-order extended Kalman filters in SOC estimation accuracy.
This paper 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 (SOC) 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 linearised model. It is then demonstrated that biases in the measurements, due to sensor ageing 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 (UKF) are applied using experimental data. The different performances of the filters are explained from the point of view of observability.