TY - GEN
T1 - Online energy management strategy of fuel cell hybrid electric vehicles based on time series prediction
AU - Zhou, Daming
AU - Gao, Fei
AU - Ravey, Alexandre
AU - Al-Durra, Ahmed
AU - Simões, Marcelo Godoy
N1 - Funding Information:
This work is supported by the European Commission H2020 grant EPSESA (H2020-TWINN-2015), EU Grant agreement No: 692224.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/26
Y1 - 2017/7/26
N2 - A suitable energy management strategy is essential to reduce hydrogen consumption of fuel cell hybrid electric vehicles (FCHEVs) and limiting its negative effects. Many different methods for the energy management of FCHEVs are being used. As common used optimization-based approaches, the genetic algorithm and dynamic programming (DP) are frequently used in global optimization control to improve the efficiency and performance of energy storages in FCHEVs. However, these offline strategies cannot be applied to the vehicle if the driving cycle is not known or predicted. In this paper, an online energy management strategy is proposed base d on time series prediction model nonlinear autoregressive neural network (NARANN). Then, a novel approach using the moving window method is applied, in order to 1) train the prediction model and 2) iteratively perform offline optimization-based strategies. In the proposed strategy, the prediction model can provide accurate online driving cycle. Based on these dynamically predicted driving profiles, the offline optimization-based strategies can be easily applied. The proposed strategy is simulated using actual driving cycle data from an electric Golf Cart. Simulation results show that the effectiveness of the proposed method.
AB - A suitable energy management strategy is essential to reduce hydrogen consumption of fuel cell hybrid electric vehicles (FCHEVs) and limiting its negative effects. Many different methods for the energy management of FCHEVs are being used. As common used optimization-based approaches, the genetic algorithm and dynamic programming (DP) are frequently used in global optimization control to improve the efficiency and performance of energy storages in FCHEVs. However, these offline strategies cannot be applied to the vehicle if the driving cycle is not known or predicted. In this paper, an online energy management strategy is proposed base d on time series prediction model nonlinear autoregressive neural network (NARANN). Then, a novel approach using the moving window method is applied, in order to 1) train the prediction model and 2) iteratively perform offline optimization-based strategies. In the proposed strategy, the prediction model can provide accurate online driving cycle. Based on these dynamically predicted driving profiles, the offline optimization-based strategies can be easily applied. The proposed strategy is simulated using actual driving cycle data from an electric Golf Cart. Simulation results show that the effectiveness of the proposed method.
KW - Energy management strategy
KW - Fuel cell hybrid electric vehicles (FCHEVs)
KW - Moving window method
KW - Nonlinear autoregressive neural network (NARANN)
UR - http://www.scopus.com/inward/record.url?scp=85028586631&partnerID=8YFLogxK
U2 - 10.1109/ITEC.2017.7993256
DO - 10.1109/ITEC.2017.7993256
M3 - Conference contribution
AN - SCOPUS:85028586631
T3 - 2017 IEEE Transportation and Electrification Conference and Expo, ITEC 2017
SP - 113
EP - 118
BT - 2017 IEEE Transportation and Electrification Conference and Expo, ITEC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Transportation and Electrification Conference and Expo, ITEC 2017
Y2 - 22 June 2017 through 24 June 2017
ER -