Robust Parameter Estimation Algorithms for Nonlinear Aftertreatment Models

An easy-to-use implementation of a Differential Evolution Based Stochastic Optimizer (DEBSO) for nonlinear, multi-modal problems is presented. Using two case studies, we demonstrate that DEBSO is (1) more effective and (2) less sensitive to user defined initial guess values, in finding the global optimum, as compared to that of a gradient based deterministic optimizer. Results from using DEBSO for construction of empirical catalyst maps from pulsator data and estimation of parameters in a diesel oxidation catalyst model are also presented. The effectiveness and efficiency of DEBSO has been compared to other evolution-based optimizers in Appendix A.