The current steady-state fuel consumption models are not optimal due to their large estimation error, complex structure, excess model coefficients, and relatively low predicting accuracy of fuel consumption. Based on experimental data of a passenger car’s fuel consumption, this study analyzes advantages and disadvantages of existing models, proposes a unified expression of the transient variable polynomial fuel-consumption model, introduces the Bayesian information criterion (BIC) for model selection, and establishes an instantaneous fuel-consumption model with simple structure and high accuracy. The simulation results show that, compared with the BIT-TFCM models, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed model are 6.27% and 6.43% smaller, respectively, on average. The proposed model has the advantages of simple structure, high precision, less influence from working conditions, stable performance, and easy prediction of the fuel consumption of future working conditions, hence providing a foundation for economic path planning.

目前的稳态油耗模型存在估计误差大、结构复杂、模型系数过大、油耗预测精度相对较低等问题，并非最优。本研究基于乘用车油耗实验数据，分析现有模型的优缺点，提出暂态变量多项式油耗模型的统一表达，引入贝叶斯信息准则（BIC）进行模型选择，建立瞬时油耗模型，结构简单，精度高。仿真结果表明，与BIT-TFCM模型相比，所提模型的均方根误差（RMSE）和平均绝对百分比误差（MAPE）平均分别小6.27%和6.43%。