A dynamic competitive velocity prediction method based on Markov state space reconstruction

Abstract

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEVs). A key technology in PEM is velocity prediction and its accuracy greatly affects the effectiveness of a PEM strategy. This paper proposes a novel dynamic competitive velocity prediction method based on Markov state space (SS) reconstruction. The basic Markov model is introduced and its performance is fully evaluated. The Markov SS is designed by the K-Means++ clustering method to support online reconstruction. The transition probability matrix (TPM) is updated to adapt to the actual driving scenario. The dynamic competitive prediction method combines the basic and the reconstructed Markov models to achieve better performance. The velocity prediction performance is validated through repetitive complex driving conditions. Simulation result shows that the proposed method has superior performance in both prediction accuracy and computing time. For the complex driving condition scenario, the proposed method can reduce prediction error by 5.7%–9.1% comparing to the basic Markov model and its computing time is about 1% of that of LSTM when the prediction horizon is 5 s.