摘要
拓展混合动力参数设计空间并节省发动机试验成本的有效方法是建立可缩放发动机模型。通过对发动机效率影响因素的分析,提出了运用神经网络建立发动机的输入可利用能和活塞平均速度与效率间的映射关系,进而建立可缩放原型机的方法。运用该方法建立了一台1.587 L汽油机的可缩放模型,将其按比例缩小得到一台1.36 L汽油机模型,实验表明1.36 L汽油机模型与1.36 L实际汽油机的转矩特性有较好的一致性。
Constructing scalable prototype model of engine is significant for saving experimentation cost and extending the design space of hybrid electric vehicles. After analyzing the factors that impact the output efficiency of engine, a scalable engine modeling method based on neural networks was presented. A scalable engine model was set up by building a BP neural network which was trained by the test data of a 1. 587 L gasoline engine. A 1.36 L gasoline engine is obtained by scaling the scalable prototype model, and it was validated by the actual tested engine data, the result show that the model＇ s performance data is consistent with the actual 1.36 L gasoline engine very well.
出处
《武汉理工大学学报》
EI
CAS
CSCD
北大核心
2008年第12期125-128,共4页
Journal of Wuhan University of Technology
基金
国家863计划资助项目(2005AA501220-3)
关键词
混合动力汽车
发动机
可缩放建模
神经网络
hybrid vehicles
internal combustion engine
scalable model
neural networks
