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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.

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.

Due to the high demand of fuel efficient construction equipment, significant research effort has been dedicated to improving excavator efficiency. Among various possibilities, methods to recuperate energy during cab swing motion have been widely examined. Electric and hydraulic hybrids designs have shown to greatly improve fuel efficiency but require drastic design changes. The redesigned systems thus require many hours of operation to offset the manufacturing costs with fuel savings. In this research, a relatively simple swing energy recuperation system is presented using an additional accumulator, fixed displacement hydraulic motor, and control valves. With the system, hydraulic fluid is stored in an accumulator, and a simple controller opens a valve to allow the stored energy to assist the engine in running the main pumps.