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Energy Efficiency Prediction Model of Heavy-duty Electric Vehicles Using Numerical Simulation

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Abstract

This paper is a study on energy efficiency prediction model of heavy-duty electric vehicles using numerical simulation. Because the current energy consumption efficiency evaluation method for electric vehicles has limitations in terms of manpower, resources, and time required, therefore a simulation model development to predict electric vehicle energy consumption efficiency is needed. Furthermore, research on heavy-duty electric vehicles is relatively insufficient compared to light-duty electric vehicles, and the need of research on heavy-duty electric vehicles is increasing. Therefore, this study develops an energy efficiency prediction model for heavy-duty electric vehicles and verifies it with the experimental results. Based on this, the applicability of the simulation method using the developed prediction model to the current energy efficiency evaluation management system for heavy-duty electric vehicles was reviewed. To verify the accuracy of the simulation model, the dynamometer test results were compared to simulation results, and the UDS protocol was used to acquire internal data for heavy-duty electric vehicles. Reliability of simulation model was secured by comparing data such as motor speed, battery voltage/current, state of charge and total driving distance of the test vehicles with data from the dynamometer experiment and the simulation model.

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Abbreviations

F x,y,f,ax :

front axis load (N)

F x,y,r,ax :

rear axis load (N)

α U :

inclination (rad)

α V :

vehicle acceleration (m/s2)

F v,lift,f :

lift force of front tire (N)

F v,lift,r :

lift force of rear tire (N)

mv,lift,r :

vehicle weight (kg)

F total,driving :

total driving force (N)

F accel :

acceleration & deceleration force (N)

F v,air :

aerodynamic drag force (N)

F v,rr :

tire rolling resistance force (N)

F incl :

inclination force (N)

F f,pt :

friction loss in powertrain lines (N)

c w :

aerodynamic drag coefficient

A v :

frontal area (m2)

ρ air :

air density (kg/m2)

v U,V,rel :

relative velocity (km/h)

L :

vertical load acted on wheel (lbs)

P :

inflation pressure of the tire (psi)

V :

vehicle velocity (mph)

M B :

braking torque (kgf·m)

p B :

braking pressure (Pa)

A B :

brake piston surface (m2)

η B :

efficiency

μ B :

friction coefficient

r B :

effective friction radius (m)

c B :

specific brake factor

η f :

final gear efficiency

η g :

gear box efficiency

r f :

final gear ratio

r g :

gear box ratio

USA:

United States of America

EU:

European Union

UDS:

unified diagnostic services

SCT:

single cycle test

MCT:

multi cycle test

SMCT:

short multi cycle test

UDDS:

urban dynamometer driving schedule

HWFET:

highway fuel economy test

HWVC:

world harmonized vehicle cycle

CSC:

constant speed cycle

SOC:

state of charge

SAE:

society of automotive engineers

BEV:

battery electric vehicle

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Acknowledgement

This work supported by the Korea energy agency.

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Authors and Affiliations

  1. School of Automotive Engineering, Kyungpook National University, Sangju, 37224, Korea

    Jingeun Song

  2. Department of Automotive Engineering, Korea National University of Transportation, Chungju, 27469, Korea

    Junepyo Cha

  3. School of Mechanical and Automotive Engineering, Hanyang Cyber University, Seoul, 04763, Korea

    Mingi Choi

Authors
  1. Jingeun Song
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  2. Junepyo Cha
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  3. Mingi Choi
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Correspondence to Mingi Choi.

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Song, J., Cha, J. & Choi, M. Energy Efficiency Prediction Model of Heavy-duty Electric Vehicles Using Numerical Simulation. Int.J Automot. Technol. 23, 1529–1536 (2022). https://doi.org/10.1007/s12239-022-0133-8

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  • DOI: https://doi.org/10.1007/s12239-022-0133-8

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