Comparison of velocity forecasting strategies for predictive control in HEVS
Paper i proceeding, 2014

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANNbased method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The sensitivity of the prediction precision and computational cost on tuning parameters in examined for each forecasting strategy. Validation results show that the ANN-based velocity predictor exhibits the best overall performance with respect to minimizing fuel consumption.


C. Sun

Beijing Institute of Technology

Xiaosong Hu

Signaler och system, System- och reglerteknik, Reglerteknik

S.J. Moura

UC Berkeley

F. Sun

Beijing Institute of Technology

ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; San Antonio; United States; 22 October 2014 through 24 October 2014

Vol. 2 Art. no. 6031-