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Comparison of velocity forecasting strategies for predictive control in HEVS

C. Sun ; Xiaosong Hu (Institutionen för signaler och system, Reglerteknik) ; S.J. Moura ; F. Sun
ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; San Antonio; United States; 22 October 2014 through 24 October 2014 Vol. 2 (2014), p. Art. no. 6031.
[Konferensbidrag, refereegranskat]

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.

Denna post skapades 2015-06-15. Senast ändrad 2015-11-09.
CPL Pubid: 218366


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Institutioner (Chalmers)

Institutionen för signaler och system, Reglerteknik (2005-2017)



Chalmers infrastruktur