| Application of Hybrid Engine Modeling Method Based on Neural Network Group and PSO with Adaptive Inertia Factor in Engine Calibration |
| Xiuyong Shi1,2, Jiande Wei1, Haoyu Wang1, Hua Liu1,2, Degang Jiang1 |
1School of Automotive Studies, Tongji University, Shanghai, 201804, China
2Nanchang Automotive Institute of Intelligence and New Energy, Nanchang, 330052, China |
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Received: August 9, 2024; Revised: September 12, 2024 Accepted: October 23, 2024. Published online: November 7, 2024. |
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| ABSTRACT |
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The traditional hybrid engine calibration method has low efficiency and high cost of manpower and material resources, which can not meet the calibration requirements of complex engine electronic control system. The calibration method based on a mathematical model can greatly reduce the test workload and improve efficiency. Therefore, the black-box model of the engine is constructed by using the results of Spearman correlation analysis, and nine variables are selected as input, at the same time five variables are used as outputs. The improved RSR-BPNNG neural network group method is used to construct the hybrid engine economy and emission model. The model prediction results show that the R2 value of fuel consumption prediction reaches 0.9975, and the R2 value of NOx emission prediction reaches 0.9933, which achieves high precision modeling. On this basis, the performance of the engine under the WHSC cycle is simulated and optimized by the improved adaptive PSO algorithm. The optimization results show that the NOx emission of the engine is reduced by 8.16%, and the fuel consumption is reduced by 4.55%. |
| Key Words:
Hybrid vehicles · Engine calibration · Neural network · Particle swarm optimization algorithm |
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Corresponding Author.
Jiande Wei , Email. 
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