NOx Emission Prediction Model of Heavy-Duty Vehicle Based on Dynamic Improved Genetic Particle Swarm-BP Network

QIAN Feng; MA Cheng; ZHU Neng; WANG Mingda; WANG Jiguang; XU Xiaowei

Vehicle Engine ›› 2023, Vol. 0 ›› Issue (5) : 63-71.

NO_x Emission Prediction Model of Heavy-Duty Vehicle Based on Dynamic Improved Genetic Particle Swarm-BP Network

QIAN Feng¹,MA Cheng¹,ZHU Neng¹,WANG Mingda²,WANG Jiguang³,XU Xiaowei¹

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Abstract

In order to reduce the influence of abnormal data acquisition and data coupling of OBD equipment in heavyduty vehicle NO_x emission rate monitoring and control, an emission prediction model was established based on BP neural network. In order to improve the accuracy of prediction model, the genetic particle swarm combination algorithm was introduced and dynamically improved. At the same time, PCA analysis was used to extract data features. The results show that the dynamic improved genetic particle swarm combination algorithm improves the fitness function by 5.75% and 3.37% compared with the traditional genetic algorithm and particle swarm optimization algorithm. Compared with the other nine prediction models, the dynamic improved genetic particle swarm-BP network performs best on the evaluation indexes MASE, RMSE and R², the first two are 0.024 and 0.033 6 respectively and the latter is 0.951. The prediction results are basically consistent with the original data, and the prediction model has higher prediction accuracy.

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neural network / genetic algorithm / particle swarm algorithm / nitrogen oxides / prediction model

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QIAN Feng,MA Cheng,ZHU Neng,WANG Mingda,WANG Jiguang,XU Xiaowei. NO_x Emission Prediction Model of Heavy-Duty Vehicle Based on Dynamic Improved Genetic Particle Swarm-BP Network[J]. Vehicle Engine. 2023, 0(5): 63-71