A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Abstract

An accurate fuel consumption prediction is significant to fuel-economy-oriented optimization, which can improve vehicle fuel economy. Based on the data-driven prediction framework, this paper proposes a real-time fuel consumption prediction model (RFCPM) using kernel extreme learning machine (KELM) which is optimized by fast converged grey wolf algorithm (FCGWA). A new steering function of FCGWA and a new activation function for KELM are presented to ensure the fast converge speed and higher accuracy performance. First, the characteristic variables of RFCPM are selected by reference to the model-based fuel consumption prediction method. Second, a KELM is adopted to predict fuel consumption. Third, FCGWA is adopted to select the best parameters of KELM using k-fold cross-validation method. Finally, the best model is chosen through real-vehicle test. Test results are compared with the original ELM and the wildly used WNN. The experiment shows that the proposed method significantly outperforms the original ELM and WNN in terms of both prediction accuracy and training time.