Journals

2026

1. Multi-Lane X-in-the-loop Evaluation of Connected Human-Driven and Automated Vehicles

   Jihun Han, Tyler Ard, Rongyao Wang, Yunyi Jia, Ardalan Vahidi, and Dominik Karbowski

   Control Engineering Practice, vol. 173, pp. 106988, Aug 2026

   DOI
2. Mixed-Traffic Impacts of CAVs: A Reproducible Benchmark and Multi-Aspect Evaluation Framework for Freeway Corridors

   Ziyi Zhang, Tyler Ard, Jihun Han, Dominik Karbowski, and Yanbing Wang

   Control Engineering Practice, vol. 173, pp. 106982, Aug 2026

   DOI

2025

1. Energy-Efficient Automated Driving for Everyday Maneuvers: Fundamentals to Experimentation

   Tyler Ard, Jihun Han, Prakhar Gupta, Dominik Karbowski, Yunyi Jia, and Ardalan Vahidi

   IEEE Control Systems Letters, vol. 9, pp. 9–14, 2025

   DOI
2. Validation of Energy Saving From Cooperative Driving Automation via Vehicle-in-the-Loop Tests

   Eunjeong Hyeon, Miriam Di Russo, Lu Zhan, Jongryeol Jeong, Namdoo Kim, Jihun Han, Priyash Misra, Kevin Stutenberg, and 1 more author

   ASME Letters in Dynamic Systems and Control, vol. 5, no. 1, pp. 011008, Jan 2025

   Abs DOI

   Abstract This article presents an experimental validation of energy savings achieved through cooperative driving automation (CDA) measured by vehicle-in-the-loop (VIL) testing in car-following scenarios. The impacts of different CDA classes—from status sharing to prescriptive—on vehicle energy efficiency are explored. In the experiments, a plug-in hybrid electric vehicle runs on a chassis dynamometer integrated with simulation software that creates a virtual environment. Results indicate that when agreement-seeking cooperation operates with even a minimal number of vehicles, energy can be saved by up to 5% over human driving. Our findings highlight the considerable promise of CDA technologies for enhancing energy efficiency, especially fostering research on agreement-seeking cooperation.

2024

1. Analysis of Control Behavior in Eco-Driving Speed Optimization Using Pontryagin’s Minimum Principle

   Shaowen Lyu, Liyue Yang, Daliang Shen, Jihun Han, Dominik Karbowski, and Namwook Kim

   IEEE Access, vol. 12, pp. 148893–148903, 2024

   DOI
2. Effect of Adaptive Cruise Control on Fuel Consumption in Real-World Driving Conditions

   Ayman Moawad, Matthew Zebiak, Jihun Han, Dominik Karbowski, Yaozhong Zhang, and Aymeric Rousseau

   Nature Communications, vol. 15, no. 1, pp. 10016, Nov 2024

   Abs DOI

   Abstract This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects.

2023

1. Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

   Tyler Ard, Longxiang Guo, Jihun Han, Yunyi Jia, Ardalan Vahidi, and Dominik Karbowski

   IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1279–1291, Feb 2023

   DOI
2. Energy Impact of Connecting Multiple Signalized Intersections to Energy-Efficient Driving: Simulation and Experimental Results

   Jihun Han, Daliang Shen, Jongryeol Jeong, Miriam Di Russo, Namdoo Kim, Julien Jean Grave, Dominik Karbowski, Aymeric Rousseau, and 1 more author

   IEEE Control Systems Letters, vol. 7, pp. 1297–1302, 2023

   DOI
3. Fast Analytical Solver for Fuel-Optimal Speed Trajectory of Connected and/or Automated Vehicles

   Jihun Han and Jackeline Rios-Torres

   IEEE Transactions on Control Systems Technology, vol. 31, no. 6, pp. 2714–2727, Nov 2023

   DOI
4. On-Track Demonstration of Automated Eco-Driving Control for an Electric Vehicle

   Jongryeol Jeong, Ahammad Basha Dudekula, Elangovan Kandaswamy, Dominik Karbowski, Jihun Han, and Jeffrey Naber

   SAE International Journal of Advances and Current Practices in Mobility, vol. 06, no. 1, pp. 181–192, Apr 2023

   Abs DOI

   This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase). The target vehicle is a Chevrolet Bolt, an electric vehicle, which is outfitted with a drive-by-wire (DBW) system that allows external APP/BPP to command the speed of the vehicle, while the operator remains in charge of the steering wheel. The DBW is connected to a rapid prototyping unit by dSpace. This unit includes: (1) real-time software that gathers all digital and analog sensors, as well as signals from the CAN bus; (2) a simple digital twin representation of the track; and (3) automated driving controls. The digital twin representation includes virtual stop signs, speed limits, and traffic lights. The digital twin can broadcast information about current and future road environment (e.g. SPaT) based on the actual position of the vehicle on the track, and correlate that to a position in the digital twin. The automated driving controls include eco-driving controls and an additional safety-focused control layer. The experiments include five road scenarios, and three control calibrations, and each combination is repeated three times. The road scenarios are all within 3.7 km in length, corresponding to one full loop around an oval track at the American Center for Mobility in Michigan, and feature various combinations of stop signs, traffic signals, and speed limits. The control calibrations correspond to a human-driver-like baseline, non-connected automated driving, and automated driving with V2I connectivity. Test-to-test variability is within 2%, thanks to careful thermal conditioning of the vehicle prior to tests. Functionality is verified and demonstrated: no excessive jerk and no violations of traffic laws occur. Energy savings of up to 7% are demonstrated in the no-connectivity case, and up to 22% in the V2I connectivity case. These tests demonstrate the real-world energy-saving potential of automated eco-driving controls.

2021

1. Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

   Jihun Han, Dominik Karbowski, Namdoo Kim, and Aymeric Rousseau

   ASME Letters in Dynamic Systems and Control, vol. 1, no. 1, pp. 011010, Jan 2021

   Abs DOI

   Abstract Safe and energy-efficient driving of connected and automated vehicles (CAVs) must be influenced by human-driven vehicles. Thus, to properly evaluate the energy impacts of CAVs in a simulation framework, a human driver model must capture a wide range of real-world driving behaviors corresponding to the surrounding environment. This paper formulates longitudinal human driving as an optimal control problem with a state constraint imposed by the vehicle in front. Deriving analytically optimal solutions by employing optimal control theory can capture longitudinal human driving behaviors with low computational burden, and adding the state constraint can assist with describing car-following features while anticipating behaviors of the vehicle in front. We also use on-road testing data collected by an instrumented vehicle to validate the proposed human driver model for stop scenarios at intersections. Results show that vehicle stopping trajectories of the proposed model are well matched with those of experimental data.

2. Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

   Jihun Han, Daliang Shen, Dominik Karbowski, and Aymeric Rousseau

   IEEE Control Systems Letters, vol. 5, no. 6, pp. 2078–2083, Dec 2021

   DOI

2019

1. Fundamentals of Energy Efficient Driving for Combustion Engine and Electric Vehicles: An Optimal Control Perspective

   Jihun Han, Ardalan Vahidi, and Antonio Sciarretta

   Automatica, vol. 103, pp. 558–572, May 2019

   DOI

2018

1. Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

   Jihun Han, Antonio Sciarretta, Luis Leon Ojeda, Giovanni De Nunzio, and Laurent Thibault

   IEEE Transactions on Intelligent Vehicles, vol. 3, no. 2, pp. 163–172, Jun 2018

   DOI

2017

1. Synthesis of Predictive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles Based on Closed-Form Solution of Optimal Equivalence Factor

   Jihun Han, Dongsuk Kum, and Youngjin Park

   IEEE Transactions on Vehicular Technology, vol. 66, no. 7, pp. 5604–5616, Jul 2017

   DOI

2015

1. Sensitivity Analysis for Assessing Robustness of Position-Based Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicle

   Jihun Han, Dongsuk Kum, and Youngjin Park

   World Electric Vehicle Journal, vol. 7, no. 2, pp. 330–341, Jun 2015

   Abs DOI

   Under hilly road conditions, it is difficult to achieve near-optimal performance of energy management strategy (EMS) of fuel cell hybrid electric vehicle (FCHEV). In order to achieve near-optimality, optimal state reference trajectory is predicted based on future information, and thus reference tracking controller is often considered as real-time predictive EMS. There are two approaches depending on in what way the predicted reference will be used as follows: 1) position-based predictive EMS for tracking position- dependent reference, 2) time-based predictive EMS for tracking time-dependent reference. In this paper, analytical sensitivity analysis based on Pontryagin’s minimum principle (PMP) is performed to prove robustness of position-based predictive EMS with respect to velocity uncertainty. First, optimal control problem is formulated in time and position domain, and PMP approach is used to derive boundary value problem (BVP) that achieves global optimality. Then, sensitivity differential equations are developed which describe sensitivity of original BVP with respect to velocity uncertainty. Finally, these equations will be solved simultaneously with the original BVP to compute first-order sensitivity of time- and position- dependent optimal state. Results show that sensitivity of time-dependent optimal state is much bigger than that of position-dependent optimal state because velocity uncertainty can change predicted travel time, and this effect on sensitivity is significant. Therefore, predictive EMS should use current position to track position-dependent optimal state reference in terms of the robustness with respect to velocity uncertainty.

2014

1. Cooperative Regenerative Braking Control for Front-Wheel-Drive Hybrid Electric Vehicle Based on Adaptive Regenerative Brake Torque Optimization Using under-Steer Index

   J. Han, Y. Park, and Y. Park

   International Journal of Automotive Technology, vol. 15, no. 6, pp. 989–1000, Oct 2014

   DOI
2. Impact of Hilly Road Information on Fuel Economy of FCHEV Based on Parameterization of Hilly Roads

   J. Han, D. Kum, and Y. Park

   International Journal of Automotive Technology, vol. 15, no. 2, pp. 283–290, Mar 2014

   DOI
3. Optimal Adaptation of Equivalent Factor of Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles under Active State Inequality Constraints

   Jihun Han, Youngjin Park, and Dongsuk Kum

   Journal of Power Sources, vol. 267, pp. 491–502, Dec 2014

   DOI
4. A Study on How to Utilize Hilly Road Information in Equivalent Consumption Minimization Strategy of FCHEVs

   Jihun Han, Youngjin Park, Dongsuk Kum, and Seongpil Ryu

   SAE International Journal of Alternative Powertrains, vol. 03, no. 1, pp. 72–77, Apr 2014

   Abs DOI

   This paper presents an adaptation method of equivalent factor in equivalent consumption minimization strategy (ECMS) of fuel cell hybrid electric vehicle (FCHEV) using hilly road information. Instantaneous optimization approach such as ECMS is one of real-time controllers. Furthermore, it is widely accepted that ECMS achieves near-optimum results with the selection of the appropriate equivalent factor. However, a lack of hilly road information no longer guarantees near-optimum results as well as charge-sustaining of ECMS under hilly road conditions. In this paper, first, an optimal control problem is formulated to derive ECMS analytical solution based on simplified models. Then, we proposed updating method of equivalent factor based on sensitivity analysis. The proposed method tries to mimic the globally optimal equivalent factor trajectory extracted from dynamic programming solutions. Finally, simulations for various hilly roads are carried out for validation of the proposed adaptation method of equivalent factor. Results show that the proposed method generates similar equivalent factor trajectory with globally optimal equivalent factor trajectory in the specific drive condition. In conclusion, if future vehicle velocity can be assumed as average velocity such as highway driving mode, the proposed method using hilly road information is very effective in near-optimum results as well as charge-sustaining.