Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (5): 1481-1489.doi: 10.13229/j.cnki.jdxbgxb.20210915

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Hierarchical control of hybrid electric vehicle platooning based on PID and Q⁃Learning algorithm

Yan-li YIN1,2(),Xue-jiang HUANG1,Xiao-liang PAN3,Li-tuan WANG2,Sen ZHAN1,Xin-xin ZHANG1   

  1. 1.School of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China
    2.Baotou Bei Ben Heavy Vehicle Co. ,Ltd. ,Baotou 014000,China
    3.Chongqing Changan Automobile Co. ,Ltd. ,Chongqing 401120,China
  • Received:2021-09-13 Online:2023-05-01 Published:2023-05-25

Abstract:

A hierarchical control strategy based on PID and Q-Learning algorithm for hybrid electric vehicle platooning. In the upper-level controller is proposed in this paper, the speed and position information of the preceding vehicle in the platooning are obtained based on vehicle-vehicle communication,the PID controller to realize the longitudinal control is adopt and the target speed of the following vehicle is obtained. In the lower-level controller, Q-Learning is adopted to distribute the energy of the hybrid vehicle platooning according to the target speed. The simulation results show that the average vehicle spacing in the upper control is maintained at about 14 m, which can ensure good driving safety. The average fuel consumption per 100 kilometers in the lower control is only 2.57% higher than that of DP, and the offline calculation time is reduced by 23%. This strategy can not only adapt to random working condition, but can also be implemented online, which maintains basically the same fuel economy as DP.

Key words: vehicle engineering, Q-Learning, platooning, hierarchical control, hybrid electric vehicle

CLC Number: 

  • U461.8

Table 1

Main component parameters"

参 数数 值
整备质量/kg1372
迎风面积/m22
车轮半径/m0.272
主减速比4.38
变速比0.685~3.425
发动机最大功率/kW63
发动机最大转矩/(N·m)110
电机最大功率/kW10
电池容量/(A·h)6.5

Fig.1

ISG type hybrid electric vehicle structure"

Fig.2

Upper controller structure"

Fig.3

Demand power transition probability distribution diagram"

Fig.4

Optimized torque MAP diagram of ECE_EUDC+1015 working condition"

Fig.5

Optimized torque MAP diagram of UDDS+WLTP working condition"

Fig.6

Platooning following vehicle speed"

Fig.7

Platooning following vehicle displacement"

Fig.8

Platooning following vehicle spacing"

Fig.9

Change comparison of battery SOC"

Fig.10

Engine output torque of No.1 following vehicle"

Fig.11

Motor output torque of No.1 following vehicle"

Table 2

Fuel consumption per 100 kilometers based on ECE_EUDC working condition(L/100km)"

项目Q-LearningDP
领航车3.7503.642
1号跟随车3.7903.672
2号跟随车3.7833.658
平均值3.7743.657
对比/%-+3.2

Fig.12

Platooning following vehicle speed of actual working conditions"

Fig.13

Platooning following vehicle displacement of actual working conditions"

Fig.14

Platooning following vehicle spacing of actual working conditions"

Fig.15

Engine output torque of No.1 following vehicle of actual working conditions"

Fig.16

Motor output torque of No.1 following vehicle of actual working conditions"

Fig.17

Change comparison of battery SOC"

Table 3

Fuel consumption per 100 kilometers based on actual working condition"

项目Q-LearningDP
对比+2.57%
领航车3.6323.575
1号跟随车3.6213.542
2号跟随车3.5803.450
平均值3.6113.522

Table 4

Calculation time based on actual working condition"

项目离线在线
对比-23%
Q-Learning372055
DP4560
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