基于支持向量机的新能源汽车状态评估

State Assessment of New Energy Vehicle Based on Support Vector Machines

目前,燃料电池汽车正处于试验试制阶段,为了避免故障带来的安全隐患,有必要实时掌握车辆的运行状态.本文在研究燃料电池汽车运行特点的基础上,选取了合理的评估参数,并利用最小二乘支持向量机建立了其零部件评估模型.该模型需要的数据量少,泛化能力强,并通过多个现场实例分析证明了该模型的正确性和有效性.

For the pilot phase of fuel cell vehicles concerned,it is necessary to understand real-time vehicle running state,so as to avoid vehicle breakdowns caused by the security implications effectively.Based on the study of fuel cell vehicle running features,the author has selected the reasonable assessment parameters and used least square support vector machine to build a spare parts evaluation model in this article.The model not only can solve the problem of the small sample,but also have the advantages of the generalization ability.Example analyses indicates that the model is effective.