基于神经网络的重型柴油车油耗预测研究

Research on Fuel Consumption Prediction of Heavy-Duty Diesel Vehicles Based on Neural Network

为建立准确的重型柴油车油耗预测模型,使用重型柴油车实际道路行驶数据集,利用皮尔逊相关系数计算了不同因素与油耗的相关性,选取与油耗相关性较强的7个因素,利用反向传播(BP)神经网络、长短时记忆(LSTM)神经网络分别建立重型柴油车油耗预测模型。对不同行驶路段的预测结果表明,BP神经网络对各路段油耗的预测准确性存在很大差异,模型泛化能力差,LSTM神经网络模型对各路段的预测均十分准确,模型泛化能力强。

To establish an accurate fuel consumption prediction model of heavy-duty diesel vehicles,this paper firstly used the dataset collected by heavy-duty diesel vehicles in real road driving,and Pearson correlation coefficient to calculate the correlation between different factors and fuel consumption,then selected 7 factors with strong correlation with fuel consumption,and used Back Propagation(BP)neural network and Long Short-Term Memory(LSTM)neural network to establish fuel consumption prediction models for heavy-duty diesel vehicles.The prediction results of different driving sections show that the prediction accuracy of BP neural network for fuel consumption values in different road sections differs sharply,and the generalization of the model is low,while the prediction of different road sections of the LSTM model is very accurate,and the model generalization is strong.