Name
Abstract | ||
|---|---|---|
This paper presents a predictive model of solid oxide fuel cell (SOFC) stacks for thermal management by using a support vector machine (SVM). The operating temperature of the SOFC stack is the most important variable controlled for the generation system. To carry out the control research on the stack thermal management, the predictive model of the stack temperature must be established. The SOFC stack is a nonlinear, multi-variable system that is hard to model by conventional methods. A predictive model of the stack temperature based on the least squares support vector machine (LS-SVM) with the radial basis function (RBF) is presented, which is a powerful tool to predict how a SOFC stack will behave under different operating conditions. Checked by the experimental data, the model can be established fast and the predicting accuracy is high, which applies to the research on the online predictive control strategy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICNC.2013.6818076 | 2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) |
Keywords | Field | DocType |
solid oxide fuel cell (SOFC), temperature, thermal management, predictive model, Least squares support vector machine (LS-SVM) | Automotive engineering,Operating temperature,Mathematical optimization,Radial basis function,Nonlinear system,Least squares support vector machine,Stack (abstract data type),Computer science,Control theory,Support vector machine,Model predictive control,Solid oxide fuel cell | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-ying Zhang | 1 | 0 | 1.01 |
| Ying Zhang | 2 | 163 | 25.25 |
| Hong-bin Zhang | 3 | 0 | 0.34 |
| Nai-you Liu | 4 | 0 | 0.34 |