Model-based observer for direct methanol fuel cell concentration estimation by using second-order sliding-mode algorithm. (15th January 2023)

• Record Type: Journal Article
• Authors: Chen, Fengxiang; Chi, Xuncheng; Wei, Wei; Mo, Tiande; Li, Yu
• Is Part Of: Energy. Volume 263:Part D(2023)
• Publication Date: 2023-01-15
• Subjects: Direct methanol fuel cell -- Model-based observer -- Sliding-mode convergence algorithm -- Methanol concentration estimation; ca cathode -- an anode -- ch channel -- dl diffuse layer -- cl catalyst layer -- mem membrane -- st stack -- atm atmosphere; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125790
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate estimation of the real-time methanol concentration of direct methanol fuel cell (DMFC) stack is a key technique for its feedback control and optimization. However, existing data-based methods as well as voltage fluctuation methods require a large amount of data to estimate methanol concentration, which increases the burden of embedded systems. What needs to be concerned is the methanol concentration inside the stack, because it directly affects its output power, while the existing research pay more attention to the methanol supplied concentration. To this end, a model-based observer based on second-order sliding-mode (SOSM) algorithm, is proposed to estimate the real-time methanol concentration inside DMFC stack utilizing the easily measured signals, such as DMFC stack voltage, current, and temperature. To validate the proposed method, the simulation comparisons between proposed SOSM observer and existing first-order sliding-mode (FOSM), extend Kalman filter (EKF) observer were carried out under certain operation conditions. Further, experimental verifications were implemented by using a real commercial DMFC system data to verify the performance of proposed approach. The comprehensive results demonstrates that proposed SOSM observer could estimate DMFC methanal concentration with robustness and accuracy. Highlights: Establish a complete DMFC system and analyze material flow in each subsystem. Describe quantitatively nonlinear dynamic characteristics of methanol concentration in DMFC stack. Design model-based observer based on SOSM algorithm to estimate DMFC stack methanol concentration. Good robustness of SOSM observer against disturbances.