插电式混合动力汽车车速预测及能量管理策略

魏丽青; 万幸

doi:10.3969/j.issn.1001-2222.2022.03.010

车用发动机 >

2022 , Vol. 0 >Issue 3: 69 - 75

DOI: https://doi.org/10.3969/j.issn.1001-2222.2022.03.010

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插电式混合动力汽车车速预测及能量管理策略

魏丽青 ,
万幸

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（乐山职业技术学院，四川乐山 614000）

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Velocity Prediction and Energy Management Strategy of Plug-In Hybrid Electric Vehicle

WEI Liqing ,
WAN Xing

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(Leshan Vocational and Technical College，Leshan 614000，China)

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摘要

为进一步提高插电式混合动力汽车的燃油经济性，提出了一种基于组合预测模型的车速预测方法。首先，建立不同映射能力的BP神经网络车速预测模型，分别对车速进行预测，然后加权求和得到组合预测模型的车速预测结果。在此基础上，以燃油经济性最优为目标，建立了基于模型预测控制的能量管理策略，运用预测控制思想对预测时域的动力源输出进行优化分配。通过仿真表明，基于组合车速预测模型的方法比单个BP神经网络有着更高的车速预测精度。与基于规则的策略相比，该预测控制策略的燃油经济性提升了9%。

关键词： 插电式混合动力汽车; 车速; 预测; 控制策略; 能量管理

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魏丽青 , 万幸 . 插电式混合动力汽车车速预测及能量管理策略[J]. 车用发动机, 2022 , 0(3) : 69 -75 . DOI: 10.3969/j.issn.1001-2222.2022.03.010

Abstract

In order to further improve the fuel economy of plug-in hybrid electric vehicle (PHEV), the vehicle velocity prediction method was proposed based on combined prediction model. Back propagation (BP) neural network vehicle velocity prediction models with different mapping capabilities were established to predict the vehicle velocity respectively, and then the weighted summation was conducted to obtain the vehicle velocity prediction results of combined prediction model. The energy management strategy was established to obtain the optimal fuel economy based on model predictive control, and the time-domain power output of prediction was optimized by using the idea of predictive control. The simulation results show that the combined vehicle velocity prediction model has a higher vehicle velocity prediction accuracy than that of single BP neural network. Compared with the rule-based strategy, the fuel economy for the predictive control strategy improves by 9%.

Key words： PHEV; vehicle velocity; prediction; control strategy; energy management

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