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The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

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).

Plug-in electric vehicle (PHEV) has a promising prospect to reduce greenhouse gas (GHG) emission and optimize engine operating in high-efficiency region. According to the maximum electric power and all-electric range, PHEVs are divided into two categories, including “all-electric PHEV” and “blended PHEV” and the latter provides a potential for more rational energy distribution because engine participates in vehicle driving during aggressive acceleration not just by motor. However, the frequent use of engine may result in severe emissions especially in low state of charge (SOC) and ahead of catalyst light-off. This study quantitatively investigates the impact of oil viscosity and driving mode (hybrid/conventional) on oil dilution and emissions including particle number (PN).

The first several cycles determine the quality of an engine start. Low temperatures and air/fuel ratio cause incomplete combustion of the fuel. This can lead to dramatic increases in HC and PM emissions. In order to meet Euro V legislation requirements which have stricter cold start emission levels, it is critical to study the characteristics of cold and warm starting of engines in order to develop an optimized operation. The NO and THC emissions were measured by fast CLD and Fast FID gas analyzers respectively and PM in both nucleation and accumulation modes were measured by DMS500. The coolant temperature was controlled in order to guarantee the experiment repeatability. The results show that at cold start using RME60 produced higher NO and lower THC than the other tested fuels while combustion of HVO60 produced a similar level of NO but lower THC compared with mineral diesel. Meanwhile, the nucleation mode of mineral diesel was similar to RME60 but higher than HVO60.

A conventional vehicle with gasoline engine was tested on a chassis dynamometer over the new European drive-cycle (NEDC). The distributions of the engine speed and power, the throttle positions during the drive cycle are analyzed. Engine idling, acceleration and deceleration take an important proportion in the drive cycle. If engine idling is instead by engine stop, the fuel consumption will be improved by 2.27%. In an Integrated Starter Generator (ISG) system, with the assist of the starter/generator, transient operation of the engine will decrease, which reduces fuel consumption by 6%. Fuel economy will be also improved by braking regeneration and restricting operating points to an optimized region, the details are not discussed in this paper. To reduce fuel consumption further, the region where engine usually runs in urban traffic, should be paid more attention to while engine calibration.

Engine torque fluctuation is a great threat to vehicle comfort and durability. Former researches tried to solve this problem by introducing active damping system, which means the motor is controlled to produce torque ripple with just the opposite phase to that of the engine. By this means, the torque fluctuation produced by the motor and the engine can be reduced. In this paper, a new method is raised. An attempt is proposed by changing the traditional structure of the motor, making it produce ripple torque by itself instead of controlling the motor. In this way a special used ISG (Integrated Starter Generator) motor for HEV (Hybrid Electrical Vehicles) is made to achieve active damping. In order to study the possibility, a simulation, which focus on the motor instead of the whole system, is developed and series-parallel configuration is used in this simulation. As for the motor that used in this paper, four kinds of motors have been investigated and compared.

Automated parking is an efficient way to solve parking difficulties and path planning is of great concern for parking maneuvers [1]. Meanwhile, the starting region of path planning greatly affects the parking process and efficiency. The present research of the starting region are mostly determined based on a single algorithm, which limits the flexibility and efficiency of planning feasible paths. This paper, taking parallel parking and vertical parking for example, proposes a method to calculate the starting region and select the most suitable path planning algorithm for parking, which can improve the parking efficiency and reduce the complexity. The collision situations of each path planning algorithm are analyzed under collision-free conditions based on parallel and vertical parking. The starting region for each algorithm can then be calculated under collision-free conditions.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

Seatbelt and airbags provide effective occupant restraint, but are also potential to induce intrusive deformation and submarining injuries in motor vehicle crashes. To address these issues, this study puts forward a new restraint concept that applies restraint loads on shoulders and knees/femurs, i.e., the sturdiest regions of human body, via a combined use of shoulder bolster and knee bolster based on biomechanical computational analysis. The load characteristics of the two bolsters were optimized to obtain protection effectiveness superior to conventional use of seatbelt and airbag. Occupant kinematics and kinetics were taken into account, including the excursions of head, shoulders and knees, the accelerations of head and chest, and the compressions of thorax on several locations on the ribcage. The injury risk of rib fractures was monitored based on the strain levels of ribcage.

Low back pain has a higher prevalence among drivers who have long term history of vehicle operations. Vehicle vibration has been considered to contribute to the onset of low back pain. However, the fundamental mechanism that relates vibration to low back pain is still not clear. Little is known about the relationship between vibration exposure, the biomechanical response, and the physiological responses of the seated human. The aim of this study was to determine the vibration frequency that causes the increase of muscle activity that can lead to muscle fatigue and low back pain. This study investigated the effects of various vibration frequencies on the lumbar and thoracic paraspinal muscle responses among 11 seated volunteers exposed to sinusoidal whole body vibration varying from 4Hz to 30Hz at 0.4 g of acceleration. The accelerations of the seat and the pelvis were recorded during various frequency of vibrations. Muscle activity was measured using electromyography (EMG).

The primary breakup of a planar liquid sheet produced by an air-blast atomizer was studied through numerical simulations, in order to reveal physical mechanisms involved during this process. The reliability of simulations was verified by comparing the macroscopic parameters, e.g. breakup time and spatial growth rate, with experimental data. Shear instability and RT (Rayleigh-Taylor) instability were found to play important roles during the primary breakup. By analyzing the acceleration of a fluid parcel within liquid sheet using Discrete Particle Method, and measuring the wave length of transverse unstable wave, RT instability was found to be partially responsible for transverse instability. The predictions of LISA (Linearized Instability Sheet Atomization) model on breakup time were compared to experiments, and obvious differences were found to exist.

This paper has designed a real time control algorithm to use ISG motor actively compensate the torque ripple produced by the engine, to reduce torsional vibration. This paper consists of 3 parts. In the first section, this paper has introduced the research object and its modification for experiments. Then the development of control strategy is presented. The engine dynamic model is built, and real-time control with a feedforward unit and a feedback unit is derived. Encoder and cylinder pressure is used for engine torque estimator. Then the ISG motor output the counter-waveform to make the overall output smooth. In order to verify the effectiveness of the control strategy, the final section has established a test bench, where two experiments are carried out. One of the experimental conditions is to set the engine at a constant operating point, while the other is to crank the engine from 0 rpm to idle speed with ISG motor.

The electromagnetic regenerative suspension has attracted much attention recently due to its potential to improve ride comfort and handling stability, at the same time recover kinetic energy which is typically dissipated in traditional shock absorbers. The key components of a ball-screw regenerative suspension system are a motor, a ball screw and a nut. For this kind of regenerative suspension, its damping character is determined by the motor's torque-speed capacity, which is different from the damping character of the traditional shock absorber. Therefore, it is necessary to establish a systematic approach for the parameter matching of ball-screw regenerative suspension, so that the damping character provided by it can ensure ride comfort and handling stability. In this paper, a 2-DOF quarter vehicle simulation model with regenerative suspension is constructed. The effects of the inertia force on ride comfort and handling stability are analyzed.

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.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

An imperceptible engine start is critical to the acceptance of hybrid vehicles. This paper focusses on an optimal control problem that tries to reduce vibration during engine start. Efforts are made to obtain the optimal speed trajectory that could cause minimum vibration during engine start. In the first section, the target diesel powertrain is introduced. A four cylinder diesel engine is coaxially paralleled with an ISG motor. The ISG motor serves as the engine starter and engine flywheel. Its dynamic model is established using crank-link dynamics. Secondly, an index is brought out to evaluate the severity of vibration. The cylinder pressure variation is the main cause of engine torque ripple, which in turn results in engine speed fluctuation. The square of the angular acceleration is chosen as the index of vibration. The index shows a positive relation of cylinder pressure in terms of amplitude.

To characterize emission performance and engine operating conditions during high-power cold starts (HPCS), a blended plug-in hybrid electric vehicle was tested over worldwide harmonized light-duty vehicle test cycle (WLTC), and a new cycle was developed to characterize HPCS. The results showed that the engine speed and load increased dramatically to high level during HPCS under the low temperature of coolant and catalysts. The higher concentration of particle number (PN) and NOx at higher speed and load, accounted for the higher emissions during HPCS. Besides, the cumulative PN emissions increased first and then decreased with the increasing coolant temperature.

In order to solve the problems of the shuffle caused by internal and external excitation and the difficulty in obtaining the real-time accurate engine torque during the parallel mode operation of hybrid electric vehicles, a dynamic coordination control strategy for suppressing the jitter of hybrid electric vehicles based on the closed-loop control of engine speed was proposed. The engine torque filtering control method based on the slope limit was adopted to limit the rate of change of the engine torque and reduce the impact caused by the sudden change of the engine torque; the engine speed closed-loop control method was used to take the motor speed which is easy to be measured accurately in real time as the feedback control variable, which solved the problem of the real-time accurate estimation of the engine torque online. In parallel mode, the motor torque accounts for a small proportion because the torque distribution method gives priority to the engine.

Due to the objectives of achieving high fuel efficiency and drivability performance, a dual-drive hybrid system with two motors has been developed. Various drive modes are presented based on engine status, requested driver torque and power, as well as C0 status in different working conditions. The transition control of drive mode change poses a unique challenge for the dual-drive hybrid system. This study discusses the control strategies for transitioning between drive modes. The first type of transition mode is divided into four distinct phases. In the second mode transition, there are three phases: the synchronization phase involving P1 torque intervention, the C0 lock-up phase involving frozen P1 torque control and adjustment of C0 clutch torque and pressure correlation, and finally, the torque exchange phase. The third type of transition includes a dedicated torque transition phase followed by a C0 disengaged phase and concluding with a speed synchronization phase.