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This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

The study emphasizes transitioning school buses from diesel to electric to mitigate their environmental impact, addressing challenges like limited driving range through predictive models. This research introduces a comprehensive control-oriented model for estimating auxiliary loads in electric school buses. It begins by developing a transient thermal model capturing cabin behavior, divided into passenger and driver zones. Integrated with a control-oriented HVAC model, it estimates heating and cooling loads for desired cabin temperatures under various conditions. Real-world operational data from school bus specifications enhance the model’s practicality. The models are calibrated using experimental cabin-HVAC data, resulting in a remarkable overall Root Mean Square Error (RMSE) of 2.35°C and 1.88°C between experimental and simulated cabin temperatures.

Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

As data emerges as the most valuable resource in the world, the evolution of the related data industry is progressing faster. In this study, we tried to discover effective factors for fuel cell durability by using big data analysis techniques with accumulated vehicle actual road data (de-identified Blue Link Data). Basic analysis is performed assuming factors that are expected to have a significant impact on the fuel cell durability performance, and durability factor modeling according to the clustering between driving patterns and durability performance is used to determine. Now can see the change in durability performance. By analyzing the correlation between each driving pattern and durability performance, it is possible to know the weight of the effective factor affecting the durability. If the effective factor with high weight is improved in the actual vehicle unit, the durability performance is expected to increase, and the effect will be verified through real road operation.

In case of all gasoline vehicles such as the passenger vehicle, heavy duty truck and light duty truck etc., a fuel pump is located inside the fuel tank and transfers the fuel to an engine for stable driving, however, engine stall can be occurred by low pressure fuel pump. The boiling temperature of gasoline fuel is very low, the initial boiling point is around 40°C so fuel can boil easily while driving and end boiling point is around 190°C. It boils sequentially depending on the temperature. It becomes the criteria to determine the amount of vapor released inside the fuel tank at high temperature. The main cause of engine stall at high temperature is rapid fuel boiling by increasing fuel temperature. This causes a lot of vapor. Such vapor flows into the fuel pump which leading to decrease the pump load and the current consumption of the fuel pump continuously. This ultimately results in engine stall.

Deployment of autonomous vehicles requires an extensive evaluation of developed control, perception, and localization algorithms. Therefore, increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LiDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible.

Having connectivity among ground vehicles brings about benefits in fuel economy improvement, traffic mobility enhancement and undesired emission reductions. On the other hand, Unmanned Aerial Vehicles (UAV) have proven to help in getting aerial data to end users in an affordable manner. When UAVs are equipped with cameras, they can get information about the terrain they are flying over. Moreover, using Vehicle-to-Everything (V2X) communication technologies, it is possible to form a communication link between UAVs and the connected ground vehicle networks comprising of Connected and Autonomous vehicles (CAVs). To investigate and exploit the potential benefits and use cases of a broad vehicle network, a microscopic traffic simulator modified previously by our group with the addition of nearby UAVs is used to integrate simulated Connected UAVs flying above a realistic simulation of heterogeneous traffic flow containing both CAVs and non-CAVs.

This paper suggests disturbance observer which improves performance of speed limit assist control. The nonlinear disturbance observer was designed so that disturbance caused by parameter and load uncertainties is able to be estimated exponentially. With the contribution of the observer, feed-forward and integral controllers can be omitted while improving steady-state error elimination and overshoot reduction. The acceleration observer is also designed to reduce the effect of wheel slip and changing slope. The performance of the controllers has been verified not only on flat roads, but also on wave road and rapidly changing ramps.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

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.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

As CO2 legislation tightens, the next generation of turbocharged gasoline engines must meet stricter emissions targets combined with increased fuel efficiency standards. Recent studies have shown that the following technologies offer significant improvements to the efficiency of turbocharged GDI engines: Miller Cycle via late intake valve closing (LIVC), low pressure loop cooled EGR (LPL EGR), port water injection (PWI), and cylinder deactivation (CDA). While these efficiency-improving technologies are individually well-understood, in this study we directly compare these technologies to each other on the same engine at a range of operating conditions and over a range of compression ratios (CR). The technologies tested are applied to a boosted and direct injected (DI) gasoline engine and evaluated both individually and combined.

As CO2 legislation tightens, the next generation of turbocharged gasoline engines must meet stricter emissions targets combined with increased fuel efficiency standards. Promising technologies under consideration are: Miller Cycle via late intake valve closing (LIVC), low pressure loop cooled exhaust gas recirculation (LPL EGR), port water injection (PWI), and cylinder deactivation (CDA). While these efficiency improving options are well-understood individually, in this study we directly compare them to each other on the same engine at a range of operating conditions and over a range of compression ratios (CR). For this purpose we undertake a comprehensive simulation of the above technology options using a GT-Power model of the engine with a kinetics based knock combustion sub-model to optimize the fuel efficiency, taking into account the total in-cylinder dilution effects, due to internal and external EGR, on the combustion.

This paper proposes a clutch control strategy during in-gear driving situations for Dual Clutch Transmissions (DCTs). The clutch is intentionally controlled to make small amount of a slip to identify the torque transfer capacity. The control objective of this phase is to ensure the clutch slip fairly remaining the specified value. To achieve this, the micro-slip controller is designed based on sliding mode control theory. Experimental verifications performed on onboard control system of the DCT equipped vehicle demonstrate that the proposed controller good tracking performance of the desired slip speed.

This paper introduces the advancement of Engine Idle Stop-and-Go (ISG, also known as Auto Engine Stop-Start) and Neutral Coasting Control (NCC) with utilizing Advanced Driver Assistance System (ADAS) and GPS. The ISG and the In-Neutral Coasting (also known as Sailing or Gliding) have been widely implemented in recent vehicles for improving their fuel economy. However, many drivers find them somewhat disturbing because they basically change behaviors of their cars from what they used to. This annoyance discourages usages of those functions and eventually undermines their benefit of fuel saving. In order to mitigate the problem, new ISG and NCC algorithms are proposed. As opposed to the conventional logics that rely only on driver’s pedal action, the new algorithms determine whether or not to enable those functions for the given driving condition, based on the traffic information obtained using ADAS sensors and the location data from GPS and navigation map.

Prediction of power efficiency of gear boxes has become an increasingly important research topic since fuel economy requirements for passenger vehicles are more stringent, due to not only fuel cost but also environmental regulations. Under this circumstance, the automotive industry is dedicatedly focusing on developing a highly efficient gear box. Thus, the analysis of power efficiency of gear box should be performed to have a transmission that is highly efficient as much as possible at the beginning of design stage. In this study, a program is developed to analyze the efficiency of an entire gearbox, considering all components’ losses such as gear mesh, wet clutches, bearings, oil pump and so on. The analytical models are based on the formulations of each component power loss model which has been developed and published in many existing papers. The program includes power flow analysis of both a parallel gear-train and a planetary gear-train.

It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than Deceleration Fuel Cut-Off (DFCO) - which exists in all current vehicle powertrain controllers - can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

In order to improve the vehicle’s fuel economy while in cruise, the Model Predictive Control (MPC) technology has been adopted utilizing the road grade preview information and allowance of the vehicle speed variation. In this paper, a focus is on robustness study of delivered fuel economy benefit of Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier in the literature to several noise factors, e.g. vehicle weight, fuel type etc. Further, the vehicle position is obtained via GPS with finite precision and source of road grade preview might be inaccurate. The effect of inaccurate information of the road grade preview on the fuel economy benefits is studied and a remedy to it is established.