Adaptive Sliding Mode Kalman Observer for the Estimation of Vehicle Fuel Cell Humidity

The efficiency and durability of fuel cells are affected by internal water content. Therefore, the active control of humidity is of great significance for vehicle fuel cells, especially for self-humidifying fuel cell systems. To realize fuel cell internal humidity active control, it is necessary to collect the humidity information of stack in real time, so as to carry out feedback control. However, humidity sensor has the characteristics of high cost and low durability, so it is more practical to get the feedback value of humidity by using state estimation method for high-power commercial fuel cell system such as vehicle fuel cell. However, humidity estimation is often affected by other physical or chemical dynamic processes, such as oxygen transportation and response process of electrical appliances. In order to weaken the influence of other physical or chemical dynamic processes on humidity estimation, this paper proposes an adaptive sliding mode Kalman observer (ASMK) algorithm. To directly verify the accuracy and the ability to restrain the disturbance of other physical or chemical dynamic processes, this paper realizes the humidity acquisition based on the real vehicle 120 kW fuel cell system platform using humidity sensors. Compared with the classical Kalman filter algorithm, ASMK can reduce the estimation error by 50%, and can converge to the measured value in 200s while the estimation error of Kalman filter has the steady-state estimation value about 0.1. The results show that ASMK has sufficient accuracy for engineering usage and can effectively restrain the influence of other physical or chemical dynamic processes.