A novel model-based internal state observer of a fuel cell system for electric vehicles using improved Kalman filter approach

Poor air supply system management usually leads to various degrees of oxygen starvation, which affect the durability and reliability of the proton exchange membrane fuel cell (PEMFC). Real-time internal states monitoring of the cathode side is imperative for feedback control to improve the performance of the air supply system and net power. In this paper, a nonlinear internal state observer that estimates the mass of oxygen and nitrogen inside the cathode of a vehicular PEMFC system is proposed. A lumped parameter dynamic system model for the mass transport process on the cathode side is established and parameterized using a nonlinear least-squares algorithm with a working dataset. The model-based nonlinear observer is designed based on an adaptive cubature Kalman filter (ACKF) algorithm. The adaptive adjustment is implemented to match the covariances of process noise and measurement noise in real-time. Furthermore, the singular value decomposition (SVD) is employed to replace the Cholesky decomposition of the covariance of state variables to improve the convergent stability of the iterative calculation process. The performance of the proposed SVD-ACKF observer is validated and compared with the unscented Kalman filter (UKF) observer and SVD based cubature Kalman filter (SVD-CKF) observer with no adaptive process. The results show that the SVD-ACKF observer demonstrates the best convergence and accuracy, even though various uncertainties in the initial values of the internal states and system parameters, and upon different power requirements.