基于Python的汽车运行油耗预测模型的构建

Construction of vehicle fuel consumption forecast model based on Python

运用Python语言对OBD采集的车辆运行数据搭建油耗预测模型。以车速v,发动机转速n,进气管绝对压力P,节气门位置TP,冷却液温度CT,负荷率L,怠速时间IT及加速度a等作为自变量,百公里油耗作为因变量,用SelectKbest函数将参数与因变量相关性强度进行排序并做简要分析,用基于Tensorflow的多层感知机(MLP)神经网络模型以及支持向量机(SVM)多元线性回归模型同时对油耗进行预测。支持向量机模型RMSE为0.088,MAE为0.56;Tensorflow神经网络模型RMSE为0.132,MAE为0.70。结论说明模型比较可靠,可为进一步分析汽车油耗与车辆运行状态参数之间的关系提供理论依据。

Using Python language, the fuel consumption prediction model is built based on the vehicle operation data collected by OBD.Taking vehicle running state parameters such as vehicle speed v, engine speed n, intake pipe absolute pressure P, throttle position TP, coolant temperature CT, load rate L, idle time IT, acceleration a as independent variables and 100 km fuel consumption as dependent variables, the correlation intensity between parameters and dependent variables is sorted by SelectKbest function and briefly analyzed.The(MLP) neural network model of multilayer perceptron based on Tensorflow and the multiple linear regression model of support vector machine(SVM) are used to predict the fuel consumption at the same time.Support vector machine model RMSE is 0.088, MAE is 0.56, Tensorflow neural network model RMSE is 0.132, MAE is 0.70.The results show that the two models are accurate in the prediction of fuel consumption, which can provide a theoretical basis for further elucidating the relationship between vehicle fuel consumption and vehicle running state parameters.