为了有效地预测车辆的燃油消耗，提高燃油经济性并推动节能减排，提出一种基于Hyperband-CNN-BiLSTM的机动车油耗预测方法。首先基于实际道路测试收集到的车辆运行状态数据和油耗数据，分析了影响车辆油耗的显著性因素；其次结合卷积神经网络（convolutional neural network, CNN）强大的特征提取能力和双向长短期记忆网络（bidirectional long short-term memory, BiLSTM）在处理时序数据方面的优势，构建了基于CNN-BiLSTM的车辆油耗预测组合模型；然后，为提高模型预测准确性，通过Hyperband优化算法对组合模型进行优化，并将车辆油耗影响因素作为模型输入特征，对模型进行训练，实现对车辆油耗的建模和预测；最后，选取CNN、LSTM、BiLSTM、CNN-LSTM、CNN-BiLSTM作为对比模型，对Hyperband-CNN-BiLSTM预测模型效果进行评价。结果表明，相较于其他模型，Hyperband-CNN-BiLSTM模型的平均绝对误差（mean absolute error, MAE）和均方根误差（root mean squared error, RMSE）最小，分别为0.057 69和0.119 25，R²最大，为0.991 76，模型预测效果最佳。

In order to effectively predict the fuel consumption of vehicles, improve fuel economy and promote energy saving and emission reduction, a Hyperband-CNN-BiLSTM-based motor vehicle fuel consumption prediction method was proposed. Firstly, based on the vehicle operating status data and fuel consumption data collected from the actual road test, the salient factors affecting the fuel consumption of vehicles were analyzed. Secondly, combining the powerful feature extraction capability of convolutional neural network(CNN) and the advantages of bidirectional long and short-term memory network (BiLSTM) in dealing with the time-series data, a combined model of vehicle fuel consumption prediction based on CNN-BiLSTM was constructed. Then, in order to improve the model prediction accuracy, the combined model was optimized by Hyperband optimization algorithm, and the vehicle fuel consumption influencing factors were taken as the model input features to train the model to realize the modeling and prediction of vehicle fuel consumption. Finally, CNN, LSTM, BiLSTM, CNN-LSTM and CNN-BILSTM were selected as comparison models to evaluate the effect of Hyperband-CNN-BiLSTM prediction model. The results show that compared with other models, the Hyperband-CNN-BiLSTM model has the smallest mean absolute error (MAE) and root mean squared error (RMSE). They are 0.057 69 and 0.119 25, respectively. R² is the largest (0.991 76), and the model has the best prediction effect.