Parameter identification of an SOFC model with an efficient, adaptive differential evolution algorithm

• An efficient adaptive differential evolution (IJADE) is proposed.
• IJADE is used to solve the parameter identification of SOFC model.
• Results show that our method is very promising for parameter identification.
• The V–I characteristics are consistent with both simulated and experimental data.

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as IJADE (Improved Jingqiao Adaptive DE). To verify the performance of IJADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, IJADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed.