TY - JOUR
T1 - Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control
AU - Xie, Shanshan
AU - Peng, Jiankun
AU - He, Hongwen
N1 - Publisher Copyright:
© 2017 The Authors. Published by Elsevier Ltd.
PY - 2017
Y1 - 2017
N2 - Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.
AB - Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.
KW - Energy management strategy
KW - Hybrid electric bus
KW - Markov chain
KW - Model Prediction Control
UR - http://www.scopus.com/inward/record.url?scp=85020743930&partnerID=8YFLogxK
U2 - 10.1016/j.egypro.2017.03.773
DO - 10.1016/j.egypro.2017.03.773
M3 - Conference article
AN - SCOPUS:85020743930
SN - 1876-6102
VL - 105
SP - 2672
EP - 2677
JO - Energy Procedia
JF - Energy Procedia
T2 - 8th International Conference on Applied Energy, ICAE 2016
Y2 - 8 October 2016 through 11 October 2016
ER -