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International Journal of Automotive Technology > Volume 26(3); 2025 > Article
International Journal of Automotive Technology 2025;26(3): 607-619.
doi: https://doi.org/10.1007/s12239-024-00184-7
MPC Energy Prediction Control Simulation of a Hybrid Electric Vehicle
Jinkyeom Cho1,2, Hyeongcheol Lee2
1Vehicle Control Technology Development Team, Hyundai Motor Company, 150 Hyundaiyeonguso-ro, Namyang-eup, Hwaseong-si, 18280, Gyeonggi, Korea
2Department of Electrical Engineering, Hanyang University, Seoul, 04763, Korea
PDF Links Corresponding Author.  Hyeongcheol Lee , Email. hclee@hanyang.ac.kr
Received: May 20, 2024; Revised: October 12, 2024   Accepted: October 15, 2024.  Published online: November 27, 2024.
ABSTRACT
The paper presents a model predictive control (MPC) energy prediction control function simulation environment using virtual controller technology. The MPC energy prediction function is a fuel economy improvement function applied to the hybrid control unit (HCU) of the Hyundai Santa Fe. This function uses the gradient and average speed information of the upcoming road to perform optimal driving point control of the engine and motor of the hybrid electric vehicle (HEV) based on the expected required power. The components of the simulation environment are HCU virtual controllers, vehicle models, roads, and navigation. Virtual controllers were created using virtual controller technology based on HCU mass production codes. The vehicle model is a Transmission Mounted Electric Device (TMED) system with the powertrain specifications of a Hyundai Santa Fe Hybrid. Road and navigation information was collected using actual vehicle test data. The consolidation of the simulation environment was confirmed by comparing the actual vehicle test data and simulation data, such as the vehicle model, MPC energy prediction control function and fuel economy model. As a result of the simulation, the characteristics of the MPC energy prediction control were confirmed and the fuel economy improved to similar to that of the vehicle test using a robot driver.
Key Words: Model Predictive Control · Optimal Control · Software in the Loop Simulation (SILs) · SOC Planning · Hybrid Control Unit (HCU) · Quadratic Programming · Virtual ECU

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