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汽车安全与节能学报

汽车安全与节能学报 ›› 2020, Vol. 11 ›› Issue (3): 345-354.DOI: 10.3969/j.issn.1674-8484.2020.03.010

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基于 Bayes 正则化的柴油机神经网络燃烧预测模型

  

  1. （内燃机燃烧学国家重点实验室，天津大学，天津 300072，中国）
  • 收稿日期:2020-07-07 出版日期:2020-09-30 发布日期:2020-10-20
  • 作者简介:第一作者 / First author ： 谢辉（1970—），男（汉），天津，教授。E-mail: xiehui@tju.edu.cn。 第二作者 / Second author ： 聂振华（1992—），男（汉），山东，硕士研究生。E-mail: niezhenhua@tju.edu.cn。

Predictive neural network model of diesel combustion based on Bayesian regularization

XIE Hui, NIE Zhenhua, CHEN Tao   

  1. (State Key Laboratory of Combustion of Internal Combustion Engines, Tianjin University, Tianjin 300072, China）
  • Received:2020-07-07 Online:2020-09-30 Published:2020-10-20

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摘要： 针对 Wiebe 燃烧模型需要大量的逐点参数整定、普适性较差、不具备预测性的问题，该文 提出一种建立基于 Bayes 正则化的神经网络燃烧预测模型的多重 Wiebe 放热率模型标定方法。利用 modeFRONTIER 对多重 Wiebe 燃烧模型进行部分工况的预标定，为燃烧预测模型建立提供数据； 进行工况边界参数和模型标定参数之间的敏感性分析，并利用基于 Bayes 正则化的神经网络建立两 者之间关系，赋予多重 Wiebe 燃烧模型预测性，降低燃烧模型标定工作量。结果表明：该燃烧预测 模型的平均精度达到 93.2%，部分工况点的预测精度达到 97% 以上，表明该神经网络燃烧预测模型 具备较高的模型精度和模型泛化能力。

关键词: 柴油机燃烧, 多重 Wiebe 燃烧模型, Bayes 正则化算法, 神经网络, 预测模型

Abstract: A method to calibrate multiple Wiebe's heat release rate model was proposed by building predictive neural network model of combustion based on Bayesian regularization to solve the problem that Wiebe's combustion model needed to go through a lot of point-by-point parameter tuning and its universality and predictability was poor. ModeFRONTIER was used to pre-calibrate part points of the multi-Wiebe combustion model, which provided data for the establishment of combustion prediction model. The sensitivity analysis between operating condition boundary parameters and model calibration parameters was carried out, and the relationship between them was established by using the neural network based on Bayesian regularization, which endowed the multiple Wiebe combustion models with prediction and reduced the calibration workload of the combustion model. The results show that the average accuracy of the combustion prediction model is 93.2%, and the prediction accuracy of some operating points is more than 97%, manifesting that the neural network combustion prediction model has high model accuracy and model generalization ability.

Key words:  diesel combustion, multiple Wiebe combustion model, Bayesian regularization algorithm, neural network, predictive mode

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