Since the advent of the internal combustion engine, knock has been a vital issue limiting the thermal efficiency of spark ignition engines under heavy load conditions. The occurrence of knock is also directly influenced by several operating parameters simultaneously. In order to investigate the effects of multiple variables on economic performance and power performance under knock limits, this study adopts single-objective optimization and multi-objective optimization methods to optimize the engine operating parameters, including exhaust gas recirculation rate, exhaust valve timing, spark timing, and intake valve timing. The optimization aims to obtain maximum volumetric efficiency, brake mean effective pressure, and minimum brake specific fuel consumption on the knock limit. First, based on the bench test data at the operation point 2800 rpm and 11.42 bar, a one-dimensional simulation engine model is established in GT-power software and verified. Second, four engine operating parameters are input into the GT-power model as controlled parameters. The epsilon-constrained differential evolution algorithm and the multi-objective differential evolution algorithm are employed to optimize the above four parameters to minimize the knock index and the damage to engine performance due to knock suppression, respectively. Finally, the results show that the two optimization algorithms optimize four parameters. The results of the epsilon-constrained differential evolution algorithm indicate that the decreasing extent of the knock index is 73.3%. In addition, the decreasing extent of brake mean effective pressure is 10.2%. What is more, the increased brake specific fuel consumption is only 0.07%. The multi-objective differential evolution algorithm gives a set of nondominated Pareto optimal solution sets. The optimal solution has a 64.4% decrease in the knock index, a 5.78% decrease in brake mean effective pressure, and a 1.45% decrease in brake specific fuel consumption.
This article carried out a numerical investigation of knock, using the Taguchi method and the grey relational analysis method to determine the importance and the contribution rate of multiple parameters on the peak pressure in the cylinder and the knock tendency under heavy load conditions. Four parameters, namely, compression ratio, spark timing, EGR rate, and inlet temperature, were set at four levels. The simulation was designed using a design of experiment method based on Taguchi's L16 orthogonal array. The simulation results of knock tendency and peak in-cylinder pressure were analyzed by the Taguchi-Grey method. According to the analysis results of the Taguchi-Grey method, the optimal level, the importance rank, and the contribution rate of factors on the knock tendency, peak in-cylinder pressure, and equivalent response were determined. The results demonstrate that the contribution rate of compression ratio, spark timing, EGR rate, and inlet temperature to the knock tendency is 45.9%, 22.98%, 19.46%, and 11.66%, respectively. The compression ratio, spark timing, EGR rate, and inlet temperature contribution to the peak in-cylinder pressure is 40.56%, 31.03%, 24.94 and 3.47%, respectively. The optimal conditions for the minimum knock tendency and the maximum peak in-cylinder pressure are obtained at CR1 EGR4 IT1 ST1 and CR4 EGR1 IT1 ST4, respectively.
In diesel engine after-treatment control technology, the accurate real-time control of Diesel Oxidation Catalyst (DOC) outlet temperature is an important topic. To find a high-precision parameter identification algorithm for the DOC system, this paper establishes zero-dimensional (0D) and one-dimensional (1D) mathematical models of DOC, introduces Variable Forgetting Factor Least Squares(VFFRLS) and Nonlinear Least Squares parameter identification for comparison and analysis. The results show that the 0D determination coefficient R-square of Nonlinear Least Squares parameter identification results is around 0.9, the root mean square error (RSME) mean is 23.682, the R-square of 1D is mostly less than 0.9, and the mean value of RSME is 32.649; The R-square of VFFRLS algorithm is 1, and the RSME is below 0.02. Therefore, the VFFRLS algorithm is more suitable for the parameter identification of the DOC temperature model.
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