Journals

2026

1. Optimal Eco-Driving Control for Electric Vehicles: Energy Savings Analysis and Experimental Study

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

   IEEE Control Systems Letters, pp. 1–1, 2026

   Abs DOI

   Energy efficient cruise control has been well studied for combustion engine vehicles. This article extends these results to electric vehicles accounting for their regenerative capability. We employ Pontryagin’s Minimum Principle (PMP) theory, which solves the energy efficient driving problem as a boundary value problem (BVP) to generate near-optimal acceleration and braking control sequences. The algorithm was tested on a Ford Mustang Mach-E in straight-line test track experiments designed to emulate daily human driving tasks. We measured approximately 10% energy savings relative to human drivers who drove the same vehicle with the same travel time and distance.

2. 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

   Abs DOI

   Verification and validation of connected and automated vehicle (CAV) control strategies are essential before real-world deployments. However, most prior work relies on simulation, and existing experiments often focus on simplified or isolated scenarios rather than corridor-scale traffic scenarios. In this study, we enhance a track-based X-in-the-loop (XIL) testing framework and systematically evaluate human-driven vehicles (HVs), connected human-driven vehicles (CHVs), and CAVs in a two-lane urban corridor under varying traffic conditions. Using Chicago Roosevelt Road as a reference, we generate realistic multi-lane traffic, including lane changes by HVs and eco-driving behaviors of CAVs. We prepare a new electric vehicle platform with full closed-loop control, furthermore we enhance a HoloLens 2 mixed-reality interface to render multi-lane virtual traffic in the driver’s field of view. The experimental results show that connected eco-driving can reduce energy consumption for both ego-CHV and ego-CAV cases, achieving up to 21% and 14% savings, respectively, under medium traffic conditions. However, travel times increase due to driver speed adherence limitations for ego-CHVs and interactions with surrounding HVs (e.g., cut-ins and queue growth) for ego-CAVs. When CAV penetration increases to 40% (future scenarios), the downstream traffic-smoothing effect reduces queue formation and cut-ins, enabling the ego-CAV to follow smoother trajectories and achieve higher energy savings (up to 21%) without additional travel time penalty.

3. MR-HVIL: A Mixed-Reality-Based Human–Vehicle-In-the-Loop On-Road Validation Platform for Mixed Traffic Testing

   Rongyao Wang, Prakhar Gupta, Tyler Ard, Jihun Han, Dominik A. Karbowski, Ardalan Vahidi, and Yunyi Jia

   IEEE Transactions on Intelligent Transportation Systems, pp. 1–15, May 2026

   Abs DOI

   This paper introduces MR-HVIL, a novel mixed-reality-based platform designed for on-road human-vehicle-in-the-loop validation in vehicle research. MR-HVIL leverages an optical see-through mixed reality framework to seamlessly integrate real-world driving environments with virtual traffic scenarios. Unlike traditional vehicle-in-the-loop and driving simulators, our platform enhances driver experience by combining real-world physical vehicle dynamic motions with optimized virtual content overlay, ensuring a realistic and immersive validation environment. To address the challenges of synchronization and localization in outdoor environments, we develop a real-time virtual object localization and correction algorithm that integrates vehicle sensor data, enhancing mixed reality accuracy and stability. Experimental validation demonstrates the platform’s efficacy in comparing human responses to real and virtual autonomous vehicles, offering new opportunities for human-centric validation in mixed traffic scenarios. MR-HVIL paves the way for safer, more versatile, and naturalistic testing of mixed traffic systems.

4. 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

   Abs DOI

   As connected and automated vehicles (CAVs) become more prevalent, quantifying their system-level impacts in realistic mixed traffic is critical. This paper releases a reproducible benchmark and evaluation framework for longitudinal CAV control in mixed traffic simulation. We evaluate four representative longitudinal controllers across two freeway scenarios: an on-ramp corridor and a calibrated Interstate-24 corridor. Outcomes are assessed consistently from four metrics: safety, throughput and stability, fuel consumption, and interactions with other drivers. The simulation analysis proceeds in three layers: comparison of controllers, market penetration analysis, and explanation of unmodeled effects. Results show that various controllers have distinct traffic impact based on their modeling principles. Generally speaking, higher CAV penetration consistently improves throughput and stability across all controllers, but safety and fuel consumption metrics may have subtle variations across controllers and scenarios. This subtlety can be captured by our comprehensive evaluation framework where the interplay across all four metrics can be quantified. Overall, the framework provides a reproducible and extensible simulation platform for assessing any custom CAV control in various traffic scenarios.

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, Feb 2025

   Abs DOI

   Energy-efficient driving is a key advancement in the deployment of automated vehicles once safety concerns are addressed. This letter formulates the energy-efficient driving problem with constraints and explores various solution methods for common driving scenarios. The findings, rooted in theory of optimal control and Pontryagin’s Minimum Principle (PMP), offer fundamental insights into energy-efficient driving strategies in every-day driving scenarios. Analytical insights from PMP, coupled with fast analytical solution of the respective boundary value problem, enabled implementation in a real-time control system and near-optimal energy savings. The proposed approach was validated through real vehicle testing on the track, with results demonstrating that automated eco-driving can achieve significant energy savings over human drivers in basic daily driving scenarios. This letter not only highlights the effectiveness of the proposed approach but also provides practical guidance for integrating energy-efficient driving strategies into real-world automated driving and advanced driver assistance systems.

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

   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, Oct 2024

   Abs DOI

   The energy efficiency of autonomous vehicles can be improved by selecting an optimized speed profile. Energy savings can be maximized by performing control optimization with knowledge of the powertrain characteristics and future driving conditions. Previous studies have shown that Pontryagin’s minimum principle (PMP) performs well in vehicle speed optimization problems. Building on the methods proposed in previous studies, the contribution of this study is to derive meaningful observations from the concepts and results of PMP to enhance the understanding of the control problem. In particular, the switching behavior of the control mode is analyzed with supportive variables, such as ξ and mv, which dictates the changes in the control modes. Additionally, the existence of the singular control is analyzed, which helps in understanding the cruise driving in the control problem. Finally, we obtain several solutions that satisfy various boundary conditions along with a map of the reachable states, and discuss the impact of cruise driving. This is helpful for designing practical control concepts for real-world applications based on this map. Previous studies have contributed significantly to this control problem; however, this study provides a better understanding of the issue and offers guidance and inspiration for future real-world applications based on these meaningful observations.

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

   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

   Abs DOI

   Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin’s Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

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, Jan 2023

   Abs DOI

   Vehicle-to-infrastructure (V2I) communication connects vehicles and enables collision-free and energy-efficient driving, such as eco-approaches and departures at signalized intersections. An increased connectivity range can connect multiple signalized intersections and lead to long-term energy-efficient driving using richer information. However, no published studies to date provide insights into the energy-saving potential of increasing the connectivity range. In this letter, we present a V2I-enabled eco-driving control that can perform multiple traffic signal eco-approaches, and we systematically design a large-scale simulation study to quantify the energy impact of the increased V2I range for various scenarios. Simulation results show that the V2I-enabled eco-driving control can reduce energy use by up to 40%, on average, compared to the baseline, depending on road attributes and vehicle powertrain type. We validate these findings by evaluating the controller through a vehicle-in-the-loop testing platform.

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

   Abs DOI

   A longitudinal fuel-optimal speed trajectory has been found to be a control sequence of four possible modes: maximum acceleration, constant speed cruising, coasting, and maximum braking. However, a numerical optimization solver is required, which has been shown to have a tradeoff between computing efficiency and optimality. This article presents a fast analytical solver that computes the longitudinal fuel-optimal speed trajectory for connected and automated vehicles (CAVs). We formulate a longitudinal fuel-optimal control problem and transform it into a boundary value problem (BVP) through Pontryagin’s minimum principle. By analyzing the costate dynamics of the BVP, we investigate the underlying mechanism required to build the control sequences that consist of multiple modes for a given boundary condition (BC). This approach allows us to identify feasible control sequences and establish feasible criteria for BC for each sequence. Unlike BVP numerical solvers that require a good initial guess, initial costates can be analytically obtained by linking each mode that has an explicit solution after the control sequence is specified by BC. Finally, we show that CAVs equipped with the proposed solver lead to significant fuel savings for single and multiple-vehicle scenarios with different CAV penetration rates.

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

   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

   Abs DOI

   Connecting automated vehicles to traffic lights can lead to significant energy savings by enabling them to pass through intersections in an energy-efficient way without unnecessary stops. A cellular-based communication system connecting multiple traffic lights can help realize the full potential of energy-efficient driving at intersections. Thus, we propose a hierarchical speed planner that can leverage information from multiple connected traffic lights. The proposed speed planner consists of two modules: a green window selector and a reference trajectory generator. The green window selector, based on Dijkstra’s algorithm, finds a series of “green windows” for connected traffic lights that builds an energy-optimal path for vehicles to follow. The reference trajectory generator finds optimal entering times, based on the selected green window at each intersection, and then computes reference trajectories. Deriving and using analytical optimal entering speeds as a function of entering times allows us to guarantee the computational simplicity suitable for real-time implementation. We also demonstrate how to balance energy and traffic flow perspectives in the reference trajectory generator. Finally, a high-fidelity simulation framework is used to evaluate the proposed speed planner and quantify the extent to which it can save energy in various real-world urban route scenarios.

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

   Abs DOI

   This paper formulates energy efficient driving of gasoline and electric powered vehicles as optimal control problems of various complexity. We show minimizing aerodynamic drag can maximize utilization of energy available at the wheel and requires low and constant speeds. By employing optimal control theory we show periods of maximal acceleration, maximal braking, and coasting often accompany constant speed cruising to satisfy boundary conditions on the states (bang-singular-bang optimal control). In the case of gasoline engine vehicles, analytical optimal control derivations show that pulse and glide operation of the engine while cruising can further reduce fuel use (chattering optimal control). For electric vehicles (EV), quadratic rather than linear dependence of energy use on control input results in different eco-driving patterns from gasoline engine vehicles. For EVs, analytical solution to the two point boundary value optimal control problem could be obtained after model simplification which is compared to numerical solution based on a more accurate model. We also evaluate optimal control solution in the presence of state constraints for EVs. Several simulation case studies are presented to showcase the energy efficiency gains with proposed eco driving strategies.

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

   Abs DOI

   Speed advisory systems have been proposed for connected vehicles in order to minimize energy consumption over a planned route. However, for their practical diffusion, these systems must adequately take into account the presence of preceding vehicles. In this paper, a safe- and eco-driving control system is proposed for connected and automated vehicles to accelerate or decelerate optimally while guaranteeing vehicle safety constraints. We define minimum intervehicle distance and maximum road speed limit as state constraints, and formulate an optimal control problem minimizing the energy consumption. Then, an analytical state-constrained solution is derived for real-time use. A feasible range of terminal conditions is established, and such conditions are adjusted to guarantee the existence of the analytical solution. The proposed system is evaluated through simulation for various driving scenarios of the preceding vehicle. Results show that it can significantly reduce energy consumption and also avoid collision without increasing trip time. Moreover, the proposed system can serve as an energy-efficient advanced cruise control by setting a short prediction horizon.

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

   Abs DOI

   Previously, an equivalent consumption minimization strategy (ECMS) was developed that provides near-optimal performance of hybrid vehicles based on an adaptation of equivalence factor from state of charge feedback. However, under real-world driving conditions with uncertainties, such as hilly roads, ECMS requires a predictive scheme utilizing future driving information in order to prevent a loss of optimality. In this paper, we synthesize predictive ECMS in a feedforward way to adjust the equivalence factor based on its theoretical connection with future driving statistics, in a systematic manner. First, a useful noncausal adaptation strategy is extracted from dynamic programming results. Then, the inverse problem is formulated and solved to derive an explicit representation of the constant optimal equivalence factor with justified assumptions. Finally, a causal, predictive adaptation strategy using this closed-form solution is synthesized to mimic the noncausal one, and its effectiveness is evaluated for fuel cell hybrid electric vehicles. Results show that if the predicted statistical information reflects well the future driving conditions, the proposed strategy accurately estimates the constant optimal equivalence factor, including the jump behavior, thereby yielding less than 1.5% loss of fuel optimality. Moreover, this approach is extendible to other configurations.

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

   Abs DOI

   In this study, cooperative regenerative braking control of front-wheel-drive hybrid electric vehicle is proposed to recover optimal braking energy while guaranteeing the vehicle lateral stability. In front-wheel-drive hybrid electric vehicle, excessive regenerative braking for recuperation of the maximum braking energy can cause under-steer problem. This is due to the fact that the resultant lateral force on front tire saturates and starts to decrease. Therefore, cost function with constraints is newly defined to determine optimum distribution of brake torques including the regenerative brake torque for improving the braking energy recovery as well as the vehicle lateral stability. This cost function includes trade-off relation of two objectives. The physical meaning of first objective of cost function is to maximize the regenerative brake torque for improving the fuel economy and that of second objective is to increase the mechanical-friction brake torques at rear wheels rather than regenerative brake torque at front wheels for preventing front tire saturation. And weighting factor in cost function is also proposed as a function of under-steer index representing current state of the vehicle lateral motion in order to generalize the constrained optimization problem including both normal and severe cornering situation. For example, as the vehicle approaches its handling limits, adaptation of weighting factor is possible to prioritize front tire saturation over increasing the recuperation of braking energy for driver safety and vehicle lateral stability. Finally, computer simulation of closed loop driver-vehicle system based on Carsim™ performed to verify the effectiveness of adaptation method in proposed controller and the vehicle performance of the proposed controller in comparison with the conventional controller for only considering the vehicle lateral stability. Simulation results indicate that the proposed controller improved the performance of braking energy recovery as well as guaranteed the vehicle lateral stability similar to the conventional controller.

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

   Abs DOI

   Under real-life driving conditions, hilly roads are prevalent. Hilly road profile substantially influences fuel economy (FE) due to large impacts (increase or decrease) on power demand profile. Thus, the utilization of future altitude profile information has large potential to improve FE. In this paper, for optimal energy management of fuel cell hybrid electric vehicles (FCHEV), we investigate how much FE could potentially be improved when future altitude profile information is available. In particular, the simulation results are analyzed to justify the reason for this potential improvement and to identify which characteristics of hilly roads leads to large FE improvements. First of all, four statistical parameters are defined to characterize hilly roads: mean value, standard deviation (STD), distance interval (DI), and total distance. Then, several types of virtual hilly roads are generated based on various parameter combinations. In order to evaluate the potential FE improvement two energy management strategies (EMSs) are utilized: the first is Dynamic Programming, which evaluates the globally optimal FE when future hilly road information is available; the other is the Equivalent Consumption Minimization Strategy (ECMS) with adaptive equivalent factor for charge-sustenance, which represents the baseline EMS when future hilly road information is not available. The results show that downhill roads have much larger potential than uphill roads do for FE improvements when the future altitude profile is properly used for EMS. Furthermore, if the battery capacity is not large enough to handle the difference in potential energy, future hilly road information is more important to prevent violations of the maximum state-of-charge bound.

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

   Abs DOI

   Among existing energy management strategies (EMSs) for fuel cell hybrid electric vehicles (FCHEV), the equivalent consumption minimization strategy (ECMS) is often considered as a practical approach because it can be implemented in real-time, while achieving near-optimal performance. However, under real-world driving conditions with uncertainties such as hilly roads, both near-optimality and charge-sustenance of ECMS are not guaranteed unless the equivalent factor (EF) is optimally adjusted in real-time. In this paper, a methodology of extracting the globally optimal EF trajectory from dynamic programming (DP) solution is proposed for the design of EF adaptation strategies. In order to illustrate the performance and process of the extraction method, a FCHEV energy management problem under hilly road conditions is investigated as a case study. The main goal is to learn how EF should be adjusted and the impact of EF adaptation on fuel economy under several hilly road cases. Using the extraction method, the DP-based EF is computed, and its performance is compared with those of Pontryagin’s minimum principle (PMP) and conventional ECMS. The results show that the optimal EF adaptation significantly improves fuel economy when the battery SoC constraint becomes active, and thus EF must be properly adjusted under severely hilly road conditions.

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.