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A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

Author

Listed:
  • Yi Dong

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Jianmin Liu

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Yanbin Liu

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Xinyong Qiao

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Xiaoming Zhang

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Ying Jin

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Shaoliang Zhang

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Tianqi Wang

    (Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China)

  • Qi Kang

    (China Satellite Maritime Tracking and Controlling Department, Wuxi 214431, China)

Abstract

In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.

Suggested Citation

  • Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:279-:d:305737
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