•Ag/β-MnO2 was prepared by in-situ composite technique using polymeric additives.•Ag/β-MnO2 can effectively improve the discharge capacity and the cycle life.•Li2O is the main discharge product and no Li2CO3 is formed.In this paper, Ag nanoparticles decorated β-MnO2 nanorods are studied as cathode catalyst for rechargeable lithium–oxygen battery (LOB). β-MnO2 nanorods are prepared using a simple hydrothermal method based on MnO4− and the decoration of Ag nanoparticles is performed by in-situ composite technique in the presence of polymeric additives. The as-prepared materials are characterized by XRD, TEM, XPS, BET and Raman spectrum. Electrochemical charging and discharging capacity of β-MnO2 and Ag/β-MnO2 electrodes are investigated at the current density of 0.02 mA cm−2 in the voltage window of 2.0–4.0 V. LOB with Ag/β-MnO2 electrode shows an initial discharge capacity of 873 mA hg−1(electrode), with reversible charge capacity of 811 mA hg−1(electrode) while battery with only β-MnO2 has discharge capacity of 541 mA hg−1(electrode) and charge capacity of 445 mA hg−1(electrode). Ag/β-MnO2 nanocomposite electrode shows good rate capability and cycle stability. After 10 cycles, the capacity of 742 mA hg−1(electrode) is still retained at the current density of 0.02 mA cm−2 while only 219 mA hg−1(electrode) is retained at 0.5 mA cm−2. The capacity retention rate is 84.9% and 70.2% at 0.02 and 0.5 mA cm−2, respectively. During discharging, Li2O is the main discharge product and no Li2CO3 is formed. The results show that the electrochemical performance of β-MnO2 is greatly enhanced when Ag nanoparticles are introduced. And it is highly effective for decreasing the charging potential close to the theoretical value. Ag nanoparticles can enhance the electronic conductivity of the network. The study confirms that Ag/β-MnO2 catalyst is a promising effective catalyst for LOB.

The tubular solid oxide fuel cell (SOFC) stack has important parameters that need to be identified and optimized for the control of high performance. In this paper, a simple SOFC electrochemical model which its parameters need to be optimized is introduced to implement stack control for high output power. A dynamic SOFC model is built based on three sub-models to provide a large numbers simulated data and different condition for optimization. Unlike the traditional parameter optimization method--simple genetic algorithm (SGA), an improved genetic algorithm (IGA) is introduced. The proposed method shows more accuracy and validity by comparing the different results using SGA and IGA methods, the simulated data, and experimental data. The models and IGA method are adapted to control processes.

Thermal management of a solid oxide fuel cell (SOFC) stack essentially involves control of the temperature within a specific range in order to maintain good performance of the stack. In this paper, a nonlinear temperature predictive control algorithm based on an improved Takagi–Sugeon (T–S) fuzzy model is presented. The improved T–S fuzzy model can be identified by the training data and becomes a predictive model. The branch-and-bound method and the greedy algorithm are employed to set a discrete optimization and an initial upper boundary, respectively. Simulation results show the advantages of the model predictive control (MPC) based on the identified and improved T–S fuzzy model for an SOFC stack.

Thermal management for a solid oxide fuel cell (SOFC) is actually temperature control, due to the importance of cell temperature for the performance of an SOFC. An SOFC stack is a nonlinear and multi-variable system which is difficult to model by traditional methods. A modified Takagi–Sugeno (T–S) fuzzy model that is suitable for nonlinear systems is built to model the SOFC stack. The model parameters are initialized by the fuzzy c-means clustering method, and learned using an off-line back-propagation algorithm. In order to obtain the training data to identify the modified T–S model, a SOFC physical model via MATLAB is established. The temperature model is the center of the physical model and is developed by enthalpy-balance equations. It is shown that the modified T–S fuzzy model is sufficiently accurate to follow the temperature response of the stack, and can be conveniently utilized to design temperature control strategies.