Extremum seeking methods for online optimization of spark advance in alternative fueled engines

Abstract

Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This make optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of greybox extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. The ability and flexibility of the proposed framework is demonstrated through simulation examples. The approaches demonstrated may be extended to other engine inputs requiring online optimization.