The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.
The assembly consistency of a diesel engine will affect its nitrogen oxides (NOx) emission variation. In order to improve the NOx emissions of diesel engines, a study was carried out based on the assembly tolerance variation of the diesel engine’s combustion system. Firstly, a diesel engine which meets the Euro VI standards together with the experimental data is obtained. The mesh model and combustion model of the engine combustion system are built in the Converge software (version 2.4, Tecplot, Bellevue, DC, USA), and the experimental data is used to calibrate the combustion model obtained in the Converge software. Then, the four-factor and three-level orthogonal simulation experiments are carried out on the dimension parameters that include nozzle extension height, throat diameter, shrinkage diameter and combustion chamber depth. Through mathematical analysis on the experimental data, the results show that the variation of nozzle extension height and combustion chamber depth have a strong influence on NOx emission results, and the variation of combustion chamber diameter also has a weak influence on NOx production. According to the regression model obtained from the analysis, there is a quadratic function relating the nozzle extension height and NOx emissions and the amount of NOx increases with increasing nozzle extension height. The relationship between emission performance and size parameters is complex. In the selected size range, the influence of the variation of the chamber diameter on NOx is linear. The variation of the chamber depth also has an effect on NOx production, and the simulation results vary with the change of assembly tolerance variation. Thus, in the engine assembly process, it is necessary to strictly control the nozzle extension height and combustion chamber depth. The research results are useful to improve the NOx emission of diesel engine and provide a basis for the control strategy of selective catalytic reduction (SCR) devices.
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