Combining GA-SVM and NSGA-Ⅲ multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine

Yuhua Wang, Guiyong Wang, Guozhong Yao, Qianqiao Shen, Xuan Yu and Shuchao He

Energy, 2023, vol. 278, issue PA

Abstract: This research proposed a multi-objective optimization approach that combines Non-dominated Sorting Genetic Algorithms (NSGA) Ⅲ and support vector machine (SVM) to reduce diesel engine emissions while enhancing economic performance and calibration efficiency. In order to obtain accurate experimental data on diesel engines, a space-filling design method was proposed based on the prediction modeling of diesel engine performance. The SVM prediction model for diesel engine performance was established. A genetic algorithm (GA) was introduced to optimize the SVM model's penalty factor and radial basis parameters, thereby improving its prediction accuracy. The multi-objective optimization approach optimized the braking specific fuel consumption (BSFC), NOx, and CO. The results show that: the GA-SVM diesel engine performance prediction model has excellent prediction performance and generalization ability for BSFC, NOx, and CO, with R2 values of 0.981, 0.979, and 0.968, respectively. GA-SVM was used to evaluate the fitness of the NSGA-III optimal set. This not only ensures optimization accuracy but also improves working efficiency. After optimization, the BSFC of the diesel engine was reduced by 1.67%, NOx emission was reduced by 27.01%, CO emission was reduced by 19.15%, and noticeable optimization results were obtained. This work has important reference value for the automatic calibration of diesel engine control parameters, improving the economy and emission of diesel engines.

Keywords: Diesel engine; Injection parameter optimization; Multi-objective optimization; NSGA-III; SVM; Prediction model; Mathematical models (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223013592

DOI: 10.1016/j.energy.2023.127965

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