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In hybrid vehicles, the drive motor is directly connected to the drive train and the inherent drive train damping is low. When subjected to external disturbance, the low damping characteristics of the transmission system may cause torsional vibration, which will continue to oscillate the transmission system and affect the driving performance of the vehicle. In this paper, we propose a harmonic injection wheel control method based on motor speed to suppress oscillations and improve the driving performance of hybrid electric vehicles. The harmonic injection control method based on motor speed is based on Fourier transform to decompose sinusoidal harmonics based on specific order of motor speed. RLS algorithm is used to estimate the amplitude and phase, and PI control is used to calculate the compensation torque for the actual amplitude and target amplitude. Simulation and test results show that the proposed control strategy is effective in suppressing oscillations.

Ammonia (NH3), a zero-carbon fuel, has great potential for internal combustion engine development. However, its high ignition energy, low laminar burning velocity, narrow range of flammability limits, and high latent heat of vaporization are not conducive for engine application. This paper numerically investigates the feasibility of utilizing ammonia in a heavy-duty diesel engine, specifically through low-pressure direct injection (LP-DI) of hydrogen to ignite ammonia combustion. Due to the lack of a well-corresponding mechanism for the operating conditions of ammonia-hydrogen engines, this study serves only as a trend-oriented prediction. The paper compares the engine's combustion and emission performance by optimizing four critical parameters: excess air ratio, hydrogen energy ratio, ignition timing, and hydrogen injection timing. The results reveal that excessively high hydrogen energy ratios lead to an advanced combustion phase, reducing indicated thermal efficiency.

The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

To tackle the over-actuated and highly nonlinear characteristics that four-wheel-independent-steering and four-wheel-independent -driving (4WIS/4WID) vehicles exhibit when tracking aggressive trajectory, a hierarchical controller with layers of computation-intensive modules is commonly adopted. The high-level linear motion controller commands the desired state derivatives of the vehicle to meet the overall trajectory tracking objectives. Then the system dynamic is inversed by the mid-level control allocation layer and the low-level wheel control layer to map the target state derivatives to steering angle and motor torque commands. However, this type of controller is difficult to implement on the embedded hardware onboard since the nonlinear dynamic inversion is typically solved by nonlinear programming.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built.

It is very significant for autonomous vehicles to have the ability to operate beyond the stable handling limits, which plays a vital role in vehicles’ active safety and enhances riding and driving pleasure. For traditional vehicles, it is rather difficult to control the longitudinal speed, sideslip angle and yaw rate simultaneously when drifting along a given trajectory because they are under-actuated. Nevertheless, for a 4-wheel-independent-drive (4WID) vehicle, it is possible and controllable thanks to its over-actuated characteristics. This article designs a trajectory following control strategy for automated drifting of 4WID vehicles. First, a double-track 7 degree of freedom (7DOF) vehicle dynamic model is established, which incorporates longitudinal and lateral load transfer and considers nonlinear tire models. The controller which proposes a hierarchical architecture is then designed.

In contrast to a normal vehicle, a 4-wheel steer (4WS) and 4-wheel independent drive (4WID) vehicle provides more flexibilities in vehicle dynamic control and better handling performance, since both the steer angle and drive torque of each wheel can be controlled. However, for motorsports, how much lap time can be improved with such a vehicle is a problem few discussed. So, this paper focuses on the racing line optimization and lap time improvement for a 4WS &4WID vehicle. First, we optimize the racing line and lap time of three given circuits with the genetic algorithm (GA) and interior-point method, and several objective functions are compared. Next, to evaluate the lap time improvement of 4WS & 4WID, a detailed vehicle dynamic model of our 4WS & 4WID platform vehicle is built in Carsim. To follow the racing line, a path following controller which contains a PID speed controller and a model predictive control (MPC) yaw rate controller is built.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Fuel quality has a significant influence on the combustion engine operation. In recent years the increasing concerns about environmental protection, energy saving, energy security and the requirements of protecting fuel injection and aftertreatment systems have been major driving forces for the Chinese fuel specification evolution. The major property changes in the evolution of Chinese national gasoline and diesel standards are introduced and the reasons behind these changes are analyzed in this paper. The gasoline fuel development from State I to State VI-B involved a decrease of sulfur, manganese, olefins, aromatics and benzene content. The diesel fuel quality improvement from State I to State VI included achieving low sulfur fuels and a cetane number (CN) increase. Provincial fuel standards, stricter than corresponding national standards, were implemented in economically developed areas in the past.

This paper presents a two-layer torque vectoring control strategy for the Formula SAE racing car of Tsinghua University to enhance steering response, lateral stability and track performance. Firstly, the dynamic model of the existing FSAE car is built as parameters of tires, suspensions, motors and aerodynamics are measured and identified. Secondly, this paper develops a two-layer torque vectoring strategy, the upper-layer direct yaw moment (DYC) controller and the lower-layer torque distribution controller are developed in Simulink. The upper-layer sliding mode control DYC controller calculates the target additional yaw moment according to the target yaw rate based on the two-degree-of-freedom (2DOF) reference model, and the sideslip angle is constrained as well.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

An experimental investigation of non-intrusive combustion sensing was performed using a tri-axial accelerometer mounted to the engine block of a small-bore high-speed 4-cylinder compression-ignition direct-injection (CIDI) engine. This study investigates potential techniques to extract combustion features from accelerometer signals to be used for cycle-to-cycle engine control. Selection of accelerometer location and vibration axis were performed by analyzing vibration signals for three different locations along the block for all three of the accelerometer axes. A magnitude squared coherence (MSC) statistical analysis was used to select the best location and axis. Based on previous work from the literature, the vibration signal filtering was optimized, and the filtered vibration signals were analyzed. It was found that the vibration signals correlate well with the second derivative of pressure during the initial stages of combustion.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The combustion characteristics and soot emissions of three types of fuels were studied in a high pressure and temperature vessel. In order to achieve better volatility, proper cetane number and high oxygen content, the newly designed WDEP fuel was proposed and investigated. It is composed of wide distillation fuel (WD), PODE3-6 mixture (PODEn) and ethanol. For comparison, the test on WD and the mixture of PODEn-ethanol (EP) are also conducted. OH* chemiluminescence during the combustion was measured and instantaneous PLII was also applied to reveal the soot distribution. Abel transformation was adopted to calculate the total soot of axisymmetric flame. The results show that WDEP has similar ignition delays and flame lift-off lengths to those of WD at 870-920 K. But the initial ignition locations of WDEP flame in different cycles were more concentrated, particularly under the condition of low oxygen atmosphere.

The article solves the problem of reducing electric power losses of the traction induction machine rotor to prevent its overheating in nominal and high-load modes. Electric losses of the rotor power are optimized by the stabilization of the main magnetic flow of the electric machine at a nominal level with the amplitude-frequency control in a wide range of speeds and increased loads. The quasi-independent excitation of the induction machine allows us to increase the rigidity of mechanical characteristics, decrease the rotor slip at nominal loads and overloads and significantly decrease electrical losses in the rotor as compared to other control methods. The article considers the technology of converting the power of individual phases into a single energy flow using a three-phase electric machine equivalent circuit and obtaining an energy model in the form of equations of instantaneous active and reactive power balance.

Reducing the weight of the M1A2 tank by lightweighting hull, suspension, and track results in 5.1%, 1.3%, and 0.6% tank mass reductions, respectively. The impact of retrofitting with lightweight components is evaluated through primary energy demand (PED), cost, and fuel consumption (FC). Life cycle stages included are preproduction (design, prototype, and testing), material production, part fabrication, and operation. Metrics for lightweight components are expressed as ratios comparing lightweighted and unmodified tanks. Army-defined drive cycles were employed and an FC vs. mass elasticity of 0.55 was used. Depending on the distance traveled, cost to retrofit and operate a tank with a lightweighted hull is 3.5 to 19 times the cost for just operating an unmodified tank over the same distance. PED values for the lightweight hull are 1.1 to 2 times the unmodified tank. Cost and PED ratios decrease with increasing distance.

In this paper, the differences between multi-hole and single-hole spray contour under the same conditions were compared by using high-speed photography. The difference between the contour area of multi-hole and that of single-hole spray was used as a parameter to describe the degree of spray collapse. Three dimensionless parameters (i.e. degree of superheat, degree of undercooling, and nozzle pressure ratio) were applied to characterize inside-nozzle thermodynamic, outside-nozzle thermodynamic and kinetic factors, respectively. In addition, the relationship between the three dimensionless parameters and the spray collapse was analyzed. A semi-empirical equation was proposed for evaluation of the degree of collapse based on dimensionless parameters of flash and non-flash boiling sprays respectively.