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This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

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).

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

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.

This study investigates how hilly road profiles affect the optimal energy management strategy for fuel cell hybrid electric vehicle (FCHEV) with various battery sizes. First, a simplified FCHEV model is developed to describe power and energy flows throughout the powertrain and evaluate hydrogen consumption. Then, an optimal control problem is formulated to find the globally optimal energy management strategy of FCHEV over driving cycles with road elevation profile. In order to solve the optimal energy management problem of the FCHEV, Dynamic Programming, a dynamic optimization method, is used, and their results are analyzed to find out how hilly road conditions affect the optimal energy management strategies. The results show that the optimal energy management with a smaller battery tends to actively prepare (e.g. pre-charge/pre-discharge) for uphill/downhill roads in order not to violate the battery state of charge (SoC) bounds.

An electric motor for regenerative braking in front-wheel-drive hybrid electric vehicle is only connected to the front axle, and mechanical friction braking can be independently applied on each of the 4 wheels. Excessive regenerative braking only at front wheels to improve fuel economy can cause under-steer and eventually vehicle instability. Nonlinear tire characteristic may cause this vehicle instability in severe cornering with hard braking. Therefore, cooperative braking control strategy has to be considered nonlinear tire characteristic for guaranteeing the vehicle stability while enhancing the braking energy recovery. This paper is to compare the performance of cooperative braking control strategy according to consider the influence of braking force on the lateral force. Carsim™ software is used to evaluate the performance of cooperative regenerative braking control regarding to the vehicle stability and regenerative braking efficiency.