In internal combustion engines, one of the most important factors influencing performance is the combustion phase, which is represented by the crank angle of 50% burnt (CA50) in this paper. In order to optimize the performance, which is represented by the indicated mean effective pressure (IMEP), CA50 should be controlled to track its optimal reference. IMEP can be considered as a function of CA50 coupled with stochastic noise, and thus leads to a stochastic optimization problem. A gradient based stochastic approximation algorithm is adopted to solve this problem. To estimate the gradient, a sample based probabilistic gradient approximation approach is developed, in which the probabilistic guarantee of estimation accuracy can be attained by adjusting the number of samples adaptively. Finally, the experimental validation is conducted on a SI gasoline engine test bench.
['Yahui Zhang', 'Tielong Shen']
Stochastic approximation for combustion phase optimization of SI gasoline engines