한지훈 교수

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 spee

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 equiva

The pyrolysis of biomass can help produce liquid transportation fuels with properties similar to those of petroleum gasoline and diesel fuel. Argonne National Laboratory conducted a life-cycle (i.e., well-to-wheels [WTW]) analysis of various pyrolysis pathways by expanding and employing the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The WTW energy use and greenhouse gas (GHG) emissions from the pyrolysis pathways were compared with those from the basel

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 tw

Under real-world driving conditions, connected and automated vehicles (CAVs) must plan and follow an energy-optimal and collision-free speed trajectory with a high updating rate, based on available information limited by its communication range. This paper presents a speed planner using analytical closed-form optimal solutions. Using the simplest vehicle model, we derive closed-form solutions as functions of boundary conditions (BCs) and summarize them without and with pure state variable inequa

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-en

Front-wheel-drive electric vehicle has only 1 electric motor which is connected to the front drive axle. With this system constraint, regenerative braking by using an electric motor can be only applied on front wheels symmetrically. Additional mechanical friction braking can be independently applied on each of all wheels using brake by wire such as EMB (Electro-Mechanical Brake). During severe cornering with braking, excessive regenerative braking force distribution to the front axle for improvi

Eco-driving is a highly nonlinear control problem. The nonlinearities include the complex energy conversion/dissipation in the powertrain, environmental influences such as road grade and aerodynamic drag, constraints due to traffic signs, safety issues, and physical limits of the vehicle system. In recent years, researchers have increasingly revisited the Koopman operator to linearize nonlinear dynamics. This paper adopts such an approximation technique to construct the lifted state space in a d

This paper presents an adaptive, two-level control structure that makes it possible to implement a numerical optimization algorithm for eco-driving in real time. The reference governor in the higher level is characterized by a low, flexible sampling rate and adopts a receding horizon for preview and optimization. The optimization algorithm thereby finds the energy-minimizing solution, based on Pontryagin’s minimum principle (PMP), for traveling the selected route segments without colliding with

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 optim