Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (8): 2121-2129.doi: 10.13229/j.cnki.jdxbgxb.20221388

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Predictive energy saving algorithm for hybrid electric truck under high-speed condition

Yu-hai WANG1(),Xiao-zhi LI1,Xing-kun LI2   

  1. 1.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
    2.State Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,China
  • Received:2022-10-31 Online:2024-08-01 Published:2024-08-30

Abstract:

In the process of long-distance cargo transportation of heavy truck, the main driving condition of the vehicle is the high-speed cruising condition with little change in speed range. In order to further improve the fuel saving rate of the vehicle under this condition, a predictive energy saving algorithm with the objective of minimizing engine fuel consumption is proposed, which takes the P2 configuration single-axle parallel hybrid truck as the research object. Based on the change of road slope in front of the vehicle, considering the influence of speed change and energy distribution on fuel consumption, the vehicle speed and battery SOC are regarded as system state variables, and the global optimal future vehicle speed trajectory and energy distribution rules for the current road are determined by dynamic programming algorithm. It has been verified that the speed planning and energy distribution results obtained by the predictive energy saving algorithm are reasonable, and the fuel saving rates of hybrid truck with predictive energy saving algorithm are 3.19% and 6.26%, respectively compared with hybrid truck using dynamic programming algorithm under cruise control system (CCS) and pure fuel truck under predictive cruise control (PCC).

Key words: vehicle engineering, hybrid electric truck, predictive energy saving algorithm, vehicle speed planning, energy management strategy

CLC Number: 

  • U461.8

Fig.1

Hybrid power system structure diagram"

Fig.2

Engine universal characteristics map"

Fig.3

Motor efficiency map"

Fig.4

Battery charging characteristics"

Fig.5

Battery discharging characteristics"

Fig.6

Multidimensional state space diagram"

Fig.7

Driving speed trajectory of section 1"

Fig.8

Driving speed trajectory of section 2"

Table 1

Fuel consumption benchmarking results"

对比项

路段

编号

行驶里程

/km

平均车速

/(km·h-1

总油耗

/L

百公里油耗

/[L·(100 km)-1

仿真计算116.0086.65.2632.88
实车实验116.0086.65.333.13
仿真计算217.1081.75.5232.28
实车实验217.1081.75.532.16

Fig.9

Road map of real vehicle test"

Table 2

Data characteristics of experimental road sections"

实验

路段

长度

/km

最大上坡

坡度/%

最大下坡

坡度/%

最大上坡

长度/km

最大下坡

长度/km

始末高

度差/m

119.152.7-2.71.454.0027.35
220.002.7-42.152.1036.25

Fig.10

Speed planning results comparison of road section 1"

Fig.11

Speed planning results comparison of road section 2"

Fig.12

Torque distribution results of road section 1"

Fig.13

Torque distribution results of road section 2"

Table 3

Comparison of fuel consumption results"

对比项路段编号行驶里程/km

平均车速

/(km·h-1

电池SOC

初值/%

电池SOC

终值/%

总油耗

/L

百公里油耗/

[L·(100 km)-1

预见性节能算法119.1579.850505.8830.70
混动车CCS119.1580.150506.0531.59
燃油车PCC119.1580.4--6.2532.64
预见性节能算法220.0079.750506.2331.15
混动车CCS220.0080.150506.4632.30
燃油车PCC220.0080.2--6.6733.35
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