A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells

Identifying reliable and accurate parameters of a solid oxide fuel cell (SOFC) is very important to simulate and analyze its dynamic conversion behavior. In this paper, a simplified variant of competitive swarm optimizer (SCSO) is proposed to solve the parameter identification problem of SOFC models. CSO performs well especially on unimodal optimization problems. However, it is with the drawbacks of two steps forward, one step back and deviating from the promising direction, resulting in low searching efficiency when solving complex multimodal optimization problems. SCSO adopts two simplified components to conquer the drawbacks: (i) a simplified learning equation: the losers just learn from the winners excluding the mean position of the population; and (ii) a renewed way of random numbers: random numbers are renewed for each loser rather than for each dimension of each loser. SCSO is applied to a Siemen Energy cylindrical cell and a 5-kW dynamic tubular stack. In addition, the influence of weight parameter and the benefit of simplified components are also experimentally investigated. Results present that SCSO is highly competitive in terms of accuracy, robustness, convergence and statistics compared with other advanced algorithms.