期刊
ENERGY
卷 234, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.121308
关键词
Plug-in hybrid electric vehicle; Predictive energy management; Blending ensemble learning; Dynamic programming; Neural networks; Driving pattern recognition
资金
- Natural Science Foundation of Fujian Province, China [2020J01449]
- National Natural Science Foundation of China [51505086]
- Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education, China [2019KLMT06]
- Research Project of Fuzhou University Science and Education Park Development Center, China [2019-JJFDKY-10]
The fuel economy of a plug-in hybrid electric vehicle depends on battery energy usage, with ELVP and AR-SOC based on MPC EMS strategy improving fuel economy. Integration of multiple velocity prediction models enhances prediction accuracy and reduces computational cost. By using an adaptive reference SOC trajectory planning method to guide battery energy distribution, optimal torque distribution decisions were derived.
The fuel economy of a plug-in hybrid electric vehicle is largely dependent on the battery energy usage during various driving cycles. In this research, within the model predictive control (MPC) principle, an Ensemble Learning Velocity Prediction (ELVP)-based energy management strategy (EMS) considering the driving pattern Adaptive Reference State of Charge (AR-SOC) is proposed. Firstly, the existing methods including Markov chain (MC), back propagation (BP) and radial basis function (RBF) neural network (NN)-based velocity prediction models are described. Then, these models are embedded into MPC-based EMS respectively, and the validation results show that the NN performs better than the MC by comparing the prediction precision, computational cost, and resultant vehicular fuel economy. By incorporating these prior knowledges, a novel ensemble learning velocity prediction method is established by blending BP-NN and RBF-NN. Subsequently, based on the expected trip distance and the velocity prediction results, an adaptive reference SOC (AR-SOC) trajectory planning method is developed to direct the distribution of battery energy for different driving patterns. Combining with the ELVP and the AR-SOC, the MPC-based EMS derives the optimal torque-distribution decisions. Finally, the validation results indicate that the proposed strategy achieves superior fuel economy under various driving cycle compared with the benchmark strategies. (c) 2021 Elsevier Ltd. All rights reserved.
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