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Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed.

As the automotive industry faces tighter fuel economy and emission regulations, it is becoming increasingly important to improve powertrain system efficiency. One of the areas to improve powertrain efficiency is the thermal management system. By controlling how to distribute the heat rejected by the engine, especially during the warm-up stage under cold temperatures, an engine thermal management system can improve the overall energy efficiency of the powertrain system. Conventionally, engine thermal management systems have been operated by a mechanical water pump and a thermostat. However, the recent introduction of electric water pumps and electrically-controlled flow valves allow for more sophisticated control of the thermal management system. In this study, these two different thermal management system architectures are investigated by conducting simulations.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and empirical testing environments. This paper outlines a new modular simulation framework for full model- based design integration to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger.

As a viable alternative to the conventional hybrid electric vehicles, so called “mild” hybrid drivetrains are currently being implemented in production vehicles. These mild hybrid electric vehicles use an Integrated Starter Generator (ISG) to simply assist the internal combustion (IC) engine rather than drive the vehicle independently of the IC engine. Some of the production mild hybrid vehicles have been shown to achieve over a10 % increase in fuel efficiency with minimal additional costs. In this paper, we present a lookup table-based control scheme for the optimal control of the ISG and the IC engine on a mild hybrid vehicle. The developed control logic is implemented in Matlab/Simulink along with a mild hybrid vehicle model, which is based on an EPA light-duty vehicle model. The simulation results show that the optimally controlled mild hybrid vehicle has better fuel efficiency with comparable drivability when compared to a simple intuitive rule-based control strategy.

Currently, several 1D physics-based high-fidelity engine simulation software packages exist and provide reasonably accurate predictions of engine performance. However, most of the current high-fidelity engine simulation packages are developed in conventional programming languages and cannot be directly implemented in today's predominant MATLAB/Simulink simulation environment. In an effort to develop a MATLAB/Simulink-based engine simulation package, a high-fidelity 1D physics-based engine simulation model is currently being developed at The University of Alabama. The proposed model library includes various functional blocks capable of being connected in a logical manner to form a full engine system. Some of the functional blocks include a 1D unsteady flow section, cylinder valve, throttle, flow junction, cylinder, and engine dynamics. In this paper, preliminary simulation results are presented as well as descriptions of the functional blocks.

Engine and drivetrain simulation has become an integral part of the automotive industry. By creating a virtual representation of a physical system, engineers can design controllers and optimize components without producing a prototype, thus reducing design costs. Among the numerous simulation approaches, 1D physics-based models are frequently implemented due to balanced performance between accuracy and computational speed. Several 1D physics-based simulation software packages currently exist but cannot be directly implemented in MALAB/Simulink. To leverage MATLAB/Simulink's powerful controller design and simulation capabilities, a 1D physics-based engine simulation tool is currently being developed at The University of Alabama. Previously presented work allowed the user to connect engine components in a physically representative manner within the Simulink environment using a standard block connection scheme and embedded MATLAB functions.