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The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Since the amount of emitted CO2 is directly related to car fuel economy, attention is being drawn to DISI (Direct-Injection Spark-Ignition) engines, which have better fuel economy than conventional gasoline engines. However, it has been a problem that the rich air-fuel mixtures associated with fuel films during cold starts due to spray impingement produce particulate matter (PM). In predicting soot formation, it is important to predict the mixture field precisely. Thus, accurate spray and film models are a prerequisite of the soot model. The previous models were well matched with low-speed collision conditions, such as those of diesel engines, which have a relatively high ambient pressure and long traveling distances. Droplets colliding at low velocities have an order of magnitude of kinetic energy similar to that of the sum of the surface tension energy and the critical energy at which the splash occurs.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

The combustion process using two fuels with different reactivity, known as dual-fuel combustion or RCCI is mainly studied to reduce emissions while maintaining thermal efficiency compared to the conventional diesel combustion. Many studies have proven that dual-fuel combustion has a positive prospect in future combustion to achieve ultra-low engine-out emissions with high indicated thermal efficiency. However, a limitation on high-load expansion due to the higher maximum in-cylinder pressure rise rate (mPRR) is a main problem. Thus, it is important to establish the operating strategy and study the effect of in-cylinder flow motion with dual-fuel combustion to achieve a low mPRR and emissions while maintaining high-efficiency. In this research, the characteristics of gasoline-diesel dual-fuel combustion on different hardware were studied to verify the effect of the in-cylinder flow motion on dual-fuel combustion.

The measurement of spray penetration length is one of crucial tasks for understanding the characteristics of diesel spray and combustion. For this reason, many researchers have devised various measurement techniques, including Mie scattering, schlieren photography, and laser induced exciplex fluorescence (LIEF). However, the requirements of expensive lasers, complicated optics, delicate setups, and tracers that affect fuel characteristics have been disadvantages of previous techniques. In this study, the background-oriented schlieren (BOS) technique is employed to measure the vapor penetration length of diesel spray for the first time. The BOS technique has a number of benefits over the previous techniques because of its quantitative, non-intrusive nature which does not require lasers, mirrors, optical filters, or fuel tracers.

Research on HEV (hybrid electric vehicle) intracity buses has become a topic of interest because the well-known service routes of intracity buses and the frequent stop/go pattern make the energy management of the vehicle straightforward. Thus, the energy flow and the energy management of the intracity bus have been studied extensively in order to improve fuel economy. However, the HEV buses that have been studied previously were equipped with a single traction motor or with dual motors with the same capacity for the convenience of the equipment without considering the motoring or generating efficiency of the traction motor. Therefore, the energy flow from the engine/generator unit to the traction motor that has been optimized by many kinds of energy distribution strategies could not be transferred to the wheels in the most efficient manner. This paper investigates this aspect of the energy flow.

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication and automated driving capability. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green, utilizing V2I and vehicle longitudinal automation to modify the speed profile of a mid-size generic vehicle to decrease fuel consumption has been studied. Pass at Green (PaG) uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and remaining time that the phase stays active.

This paper is on the design of a steer-by-wire system for a light commercial vehicle. A hardware-in-the-loop simulation test rig with the actual rack and pinion mechanism of the light commercial vehicle under study was built for this purpose. The steer-by-wire actuator can be placed on either the second pinion, the first pinion or both in the double pinion steering test system used. The hardware and geometry of the steering test rig are identical to the implementation of the steering system in the test vehicle. Unnecessary and expensive road testing is avoided with this approach as most problems are identified and solved in the hardware-in-the-loop simulation phase conducted in the laboratory where the steering subsystem and its controller exist as hardware and the rest of the vehicle exist as a software model running in real time. Hardware-in-the-loop simulation results show the effectiveness of the proposed controller design in tracking desired steering dynamics.

This paper presents the development of a transient active control (TABC) system for the Ford Transit Connect light commercial vehicle using semi active suspensions. The control objective is to improve the ride comfort and road holding together with achieving roll and pitch stability using four semi active suspension dampers, hence called transient active body control. Semi-active control algorithms such as sky-hook, ground-hook and hybrid are applied to each suspension while the roll and pitch stabilizing controllers are designed separately and interfere with the local semi-active controllers through a supervisory control algorithm, if necessary. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed technique.

Ignition delay of the second injection of HSDI diesel engines is generally much shorter than that of the first injection because of the interaction between the radicals generated during the combustion process and the mixed gas of the second injection. Although previous Diesel combustion models could not explain this reaction, Hasse and Peters described the mass and heat transfer of the second injection and estimated the ignition delay of the second injection using two-dimensional flamelet equations. But a simulation of the two-dimensional flamelet equations requires enormous computational time. Thus, to analyze the combustion phenomena of the multiple injection mode in HSDI diesel engines effectively, the two-dimensional flamelet combustion model was modified in this study. To reduce the calculation time, two-dimensional flamelet equations were only applied near the stoichiometric region.

Modeling of unburned hydrocarbon oxidation in an SI engine was performed in engine condition using modified one-step oxidation model. The new one-step equation was developed by modifying the Arrhenius reaction rate coefficients of the conventional one-step model. The modified model was well matched with the results of detailed chemical reaction mechanism in terms of 90 % oxidation time of the fuel. In this modification, the effect of pressure and intermediate species in the burnt gas on the oxidation rate investigated and included in developed one-step model. The effect of pressure was also investigated and included as an additional multiplying factor in the reaction equation. To simulate the oxidation process of piston crevice hydrocarbons, a computational mesh was constructed with fine mesh density at the piston crevice region and the number of cell layers in cylinder was controlled according to the motion of piston.

As the real-time supplying of hydrogen-rich gas becomes possible by the advances in the on-board fuel reforming technologies, utilizations of synthetic gas in IC engines are actively studied. However, due to the lack of fundamental studies on the combustion characteristics of synthetic gas, there is no precedent for the simulation of combustion process in synthetic gas fueled SI engine. In this study, the laminar flame speeds of synthetic gas and its mixture with iso-octane were calculated under extensive initial conditions of 3,575 points derived by combinations of temperature, pressure, fraction of lower heating value of synthetic gas and air-excess ratio variations.

A gasoline homogeneous charged compression ignition (HCCI) called the controlled auto ignition (CAI) engine is an alternative to conventional gasoline engines with higher efficiency and lower emission levels. However, noise and vibration are currently major problems in the CAI engine. The problems result from fast burning speeds during combustion, because in the CAI engine combustion is controlled by auto-ignition rather than the flame. Thus, the ignition delay of the local mixture has to vary according to the location in the combustion chamber to avoid noise and vibration. For making different ignition delays, stratification of temperature or mixing ratio was tested in this study. In charge stratification, which determines the difference between the start of combustion among charges with different properties, two kinds of mixtures with different properties flow into two intake ports.

CAI engine is well known to be advantageous over conventional SI engines because it facilitates higher engine efficiency and lower emission (NOx and smoke). However, its limited operation range, large cyclic variation, and difficulty in heat release control are still unresolved obstacles. Previous studies showed that a high load range of the CAI engine is limited mainly by the combustion noise caused by a stiff pressure rise (knock), and that a low load range is also limited by the combustion instability caused by the high dilution of residual gas. In this study, the characteristics of each cycle were analyzed to find the cause of the cycle variation at the high load limit of CAI operation. Moreover, to improve combustion stability, we tested the in-cylinder fuel stratification by applying nonsymmetrical fuel injection to the intake port. Experiments were performed on a PFI single cylinder research engine equipped with dual CVVT and low lift (2 mm) cam shaft with NVO strategy.

There is an increasing trend in the worldwide automotive area towards developing hybrid electric vehicles as an intermediate solution to fulfill the new, more stringent pollutant emission level requirements set by governments. Conversion of braking energy into electrical energy stored in the battery through regenerative braking is an important aspect of hybrid electric vehicles that increases their fuel efficiency. This paper presents an electric regenerative power assisted brake algorithm developed to enhance energy efficiency of a front and rear wheel drive parallel hybrid electric commercial vehicle. The commercial vehicle used in this study is a second generation research prototype Ford Transit Parallel Hybrid Electric Van. The existing hydraulic brake system of this van was not altered for reasons of safety and reliability in the case of a problem with regenerative barking.

In a Hybrid Electric Vehicle (HEV), the main aim is to decrease the fuel consumption and emissions without significant loss of driving performance. Maximizing Overall Efficiency Strategy (MOES) algorithm, presented here, distributes the power demand among the available paths to the wheels to improve fuel economy. In MOES, the vehicle is considered as a system whose input and output are power capability of consumed fuel and actual power transferred to the road, respectively. The aim of the strategy is to maximize the overall efficiency of the vehicle determined as the ratio of output power to input power. The control algorithm and driver model were prepared within Simulink and used to drive the Carmaker model of the vehicle which is a Ford Transit hybrid electric research prototype van. Simulations were carried out in 3 modes of the vehicle; conventional mode, regenerative braking only mode and full MOES mode to analyze the role of optimization better.