Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Predicting short-term driving conditions is crucial for energy management and fuel economy improvement of plug-in hybrid electric vehicles. A fused model combining stochastic forecasting and machine learning techniques is established, incorporating Markov chain for transition probability calculation and neural network for learning driving information. Optimizing network parameters with genetic algorithm, the proposed fusion algorithm outperforms single models in prediction precision and difference distribution evaluation.