Data-Efficient Vehicular Fuel Rate Estimation Via Transfer Learning Using a Nonlinear-Autoregressive-Exogenous Neural Network

Fuel estimation is crucial for intelligent transportation systems to minimize fuel consumption, thereby reducing costs and environmental impacts. However, developing accurate data-driven fuel-estimation models requires extensive data, which is usually costly and time-consuming to collect for feet systems with a large number of vehicles. This paper presents an inductive transfer learning (TL) method for fuel rate estimation to alleviate the large fuel data measurement requirement. First, a dynamic neural network based on the Nonlinear Auto-Regressive Exogenous (NARX) approach is trained for a Peterbilt 579 class 8 haul truck as the base model, using on-road fuel consumption measured data. Afterward, using the TL method, the developed fuel-estimation model is leveraged to three different vehicles with various engine sizes and types, including a Ford F-350, a hybrid Ford Fusion, and a turbocharged Ford Escape. Extensive analyses illustrated that the transferred target models require training data as minimal as 3% (3km) of the total data used for training the base model to achieve prediction accuracy comparable to the base model. Moreover, compared to developed conventional models, the TL-based target models for the Ford Fusion, Ford Escape, and Ford F-350 demonstrate average improvements of 70%, 71%, and 73% in terms of Mean Absolute Error (MAE), Rooted Mean Squared Error (RMSE), and training time, respectively.