The linear-time varying model predictive control was proposed for the vehicle energy management control strategy based on the driver demand torque prediction aiming at the parallel hybrid electric vehicle. The driver’s demand torque in the next period was predicted according to the driver's demand torque in the previous period. The prediction accuracy of the autoregressive model within the 200 prediction step length is higher compared with the exponential function prediction method. The predicted speed sequence can be calculated from the predicted demand torque sequence according to the longitudinal dynamic formula. Then the vehicle model with nonlinear characteristics was transformed into a linear time-varying mode. The linear time-varying model predictive control algorithm was used to solve the problem. The model predictive control algorithm based on driver demand torque prediction was compared with the rule-based control algorithm and the model predictive control algorithm with known operating conditions. The comparison results show that the fuel economy of the model predictive control algorithm based on driver demand torque prediction is better under the three standard operating conditions of NEDC, UDDS and WLTC compared with the rule-based control algorithm, but there is room for improvement compared with the results of known operating condition.
Dong-dong JIANG,Dao-fei LI,Xiao-li YU. Model predictive control energy management based ondriver demand torque prediction. Journal of ZheJiang University (Engineering Science), 2020, 54(7): 1325-1334.
JOHNSON V H, WIPKE K B, RAUSEN D J. HEV control strategy for real-time optimization of fuel economy and emission [C]//SAE International. Arlington: SAE, 2000: 1543.
[3]
ANBARAN S A, IDRIS N R N, JANNATI M, et al. Rule-based supervisory control of split-parallel hybrid electric vehicle [C]//Proceedings of the 2014 IEEE Conference on Energy Conversion. Johor Bahru: IEEE, 2014: 7?12.
[4]
MUSARDO C, RIZZONI G, GUEZENNEC Y, et al A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management[J]. European Journal of Control, 2005, 11 (4/5): 509- 524
[5]
SEZER V, GOKASAN M, BOGOSYAN S A novel ECMS and combined cost map approach for high-efficiency series hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2011, 60 (8): 3557- 3570
doi: 10.1109/TVT.2011.2166981
[6]
KULIKOV I A, BAULINA E E, FILONOV A I. Optimal control of a hybrid vehicle's powertrain minimizing pollutant emissions and fuel consumption [C]//SAE 2014 Commercial Vehicle Engineering Congress. Illinois: SAE, 2014: 2372-2376.
[7]
KO J, KO S, SON H, et al Development of brake system and regenerative braking cooperative control algorithm for automatic-transmission-based hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2015, 64 (2): 431- 440
doi: 10.1109/TVT.2014.2325056
[8]
KIM N, CHA S, PENG H Optimal control of hybrid electric vehicles based on pontryagin’s minimum principle[J]. IEEE Transactions on Control Systems Technology, 2011, 19 (5): 1279- 1287
doi: 10.1109/TCST.2010.2061232
[9]
HUANG Y, WANG H, KHAJEPOUR A, et al Model predictive control power management strategies for HEVs: a review[J]. Journal of Power Sources, 2017, 341 (15): 91- 106
[10]
DI C S, WEI L, KOLMANOVSKY I V, et al Power smoothing energy management and its application to a series hybrid powertrain[J]. IEEE Transactions on Control Systems Technology, 2013, 21 (6): 2091- 2103
doi: 10.1109/TCST.2012.2218656
[11]
张洁丽. 基于模型预测控制的插电式混合动力客车能量管理策略研究[D]. 北京: 北京理工大学, 2016. ZHANG Jie-li. Energy management strategy for a plug-in hybrid electric bus based on model predictive control [D]. Beijing: Beijing Institute of Technology, 2016.
[12]
BORHAN H, VAHIDI A, PHILLIPS A M, et al MPC-based energy management of a power-split hybrid electric vehicle[J]. IEEE Transactions on Control Systems Technology, 2012, 20 (3): 593- 603
doi: 10.1109/TCST.2011.2134852
[13]
SUN C, HU X, MOURA S J, et al Velocity predictors for predictive energy management in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology, 2015, 23 (3): 1197- 1204
doi: 10.1109/TCST.2014.2359176
[14]
孙超. 混合动力汽车预测能量管理研究[D]. 北京: 北京理工大学, 2016. SUN Chao. Predictive energy management of hybrid electric vehicle powertrains [D]. Beijing: Beijing Institute of Technology, 2016.
[15]
曾祥瑞, 黄开胜, 孟凡博 具有实时运算潜力的并联混合动力汽车模型预测控制[J]. 汽车安全与节能学报, 2012, 3 (2): 165- 172 ZENG Xiang-rui, HUANG Kai-sheng, MENG Fan-bo Model predictive control for parallel hybrid electric vehicles with potential real-time capability[J]. Automotive Safety and Energy, 2012, 3 (2): 165- 172
doi: 10.3969/j.issn.1676-8484.2012.02.010
IYAMA H, NAMERIKAWA T. Fuel consumption optimization for a power-split HEV via gain-scheduled model predictive control [C]//2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan. Sapporo: IEEE, 2014.
[19]
XU Xiao-kang, PENG Jun, ZHANG Rui, et al Adaptive model predictive control for cruise control of high-speed trains with time-varying parameters[J]. Journal of Advanced Transportation, 2019, (5): 1- 11
