An Adaptive Joint Operating Parameters Optimization Approach for Active Direct Methanol Fuel Cells

Abstract

The operating parameters of the active direct methanol fuel cell (DMFC) are essential factors that affect cell performance. However, it is challenging to maintain the optimal maximum output power density due to the system’s complexity, the operating conditions variation, and the correlations between those parameters. This paper proposes an adaptive joint optimization method for fuel cell operating parameters. The methods include the adaptive numerical simulation of the operation parameters and the optimization for fuel cell performance. Based on orthogonal tests, a BP neural network is used to build a performance evaluation model that can quantify the influence of the operating parameters on fuel cell performance. The optimal combination of operating parameters for the fuel cell is obtained by a whale optimization algorithm (WOA) through the evaluation model. The experimental results show that the evaluation model could respond accurately and adaptively to the cell operating conditions under different operating conditions. The optimization algorithm improves the maximum power density of the fuel cell by 8.71%.