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Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

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.

The mechanical behavior of polyvinyl butyral (PVB) film plays an important role in windshield crashworthiness and pedestrian protection and should be depth study. In this article, the uniaxial tension tests of PVB film at various strain rates (0.001 s-1, 0.01 s-1, 0.1 s-1, 1 s-1) and temperatures (-10°C, 0°C, 10°C, 23°C, 40°C, 55°C, 70°C) are conducted to investigate its mechanical behavior. Then, temperature and strain rate dependent viscoelastic characteristics of PVB are systematically studied. The results show that PVB is a kind of temperature and strain rate sensitive thermal viscoelastic material. Temperature increase and strain rate decrease have the same influence on mechanical properties of PVB. Besides, the mechanical characteristics of PVB change non-linearly with temperature and strain rate. Finally, two thermal viscoelastic constitutive model (ZWT model and DSGZ model) are suggested to describe the tension behavior of PVB film at various strain rates and temperatures.

Threat assessment (TA) method is vital in the decision-making process of intelligent vehicles (IVs), especially for ADAS systems. In the research of TA, the probabilistic threat assessment (PTA) method is acting an increasing role, which can reduce the uncertainties of driver’s maneuvers. However, the driver behavior model (DBM) used in present PTA methods was mainly constructed by limited data or simple functions, which is not entirely reasonable and may affect the performance of the TA process. This work aims to utilize crash data extracted from Event Data Recorder (EDR) to establish more accurate DBM and improve the current PTA method in rear-end situations. EDR data with responsive maneuvers were firstly collected, which were then employed to construct the initial DBM (I-DBM) model by using the multivariate Gaussian distribution (MGD) framework. Besides, the model was further subdivided into six parts by two important risk indicators, Time-to-collision (TTC) and velocity.

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.

Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.

Generally, noise will occur during steering with the hydraulic power steering system (hereinafter HPS). The noise producing in the rotary valve takes up a big proportion of the total one. To study the noise in the control valve, 2-D meshes of the flow field between the sleeve and the rotor were set up and a general CFD code-Fluent was used to analyze the flow inside the valve. The areas where the noise may be occurred were shown and some suggestions to silence the noise were given.

An energy management strategy is needed to optimally allocate the driver's power demands to different power sources in the fuel cell hybrid vehicles. The driver's power demand is modelled as a Markov process in which the transition probabilities are estimated on the basis of the observed sample paths. The Markov Decision Process (MDP) theory is applied to design a stochastic energy management strategy for fuel cell hybrid vehicles. This obtained control strategy was then tested on a real time simulation platform of the fuel cell hybrid vehicles. In comparison to the other 3 strategies, the constant bus voltage strategy, the static optimization strategy and the dynamic programming strategy, simulations in the Beijing bus driving cycle demonstrate that the obtained stochastic energy management strategy can achieve better performance in fuel economy in the same demand of dynamic.

Electromagnetic Valve Actuation (EVA) is considered to be a potential substitute of conventional valvetrains for automotive engines. However, valve quiet-seating (soft-landing) is difficult to be achieved. The EVA system and hence its’ mathematic model is nonlinear. Therefore, when linear control is used for EVA, firstly, the model has to be linearized at an equilibrium point through Taylor expansion. Consequently, the linearized model and control are valid only for a small range around the equilibrium point. This paper presents a control strategy for the whole transition of EVA, which combines exact linearization with Linear Quadratic Regulator (LQR). Firstly, the nonlinear EVA model is transformed to be linear in a new coordinate by using exact linearization, so the nonlinear model is not involved. Then the exact-linearized model is used for the EVA control with LQR.

Due to the high cost of torque sensors, a calculation model of transient torque is required for real-time coordinating control purpose, especially in hybrid electric powertrains. This paper presents a feedforward calculation method based on mean value model of turbocharged non-EGR diesel engines. A fitting variable called fuel coefficient is defined in an affine relation between brake torque and fuel mass. The fitting of fuel coefficient is simplified to depend only on three variables (engine speed, boost pressure, injected fuel mass). And a two-layer feedforward neural network is utilized to fit the experimental data. The model is validated by load response test and ETC (European Transient Cycle) transient test. The RMSE (root mean square error) of the brake torque is less than 3%.

Studying traffic accidents by using naturalistic driving data has become increasingly appealing for its potential benefits in improving road safety. This paper presents findings from a field test which has been conducted on 50 taxis in the urban areas of Beijing for 10 months using Video Drive Recorders (VDRs). The VDR used in this study could record the information of vehicle front view video, vehicle states, as well as driver operations immediately before and after an event. The drivers were given no specific instructions during the test, and the instrumentation for data collection was unobtrusive. Important safety-relevant parameters, such as vehicle speed, pre-event maneuver, time headway, time-to-collision, and driver reaction time, were calculated with precision. Based on these parameters, an analysis into features and causes of rear-end conflicts is performed.

Atomization of fuel sprays is a key factor in controlling the combustion quality in the direct-injection engines. In this present work, the effect of saturation ratio (Rs) on the near nozzle spray patterns of ethanol was investigated using an ultra-high speed imaging technique. The Rs range covered both flash-boiling and non-flash boiling regions. Ethanol was injected from a single-hole injector into an optically accessible constant volume chamber at a fixed injection pressure of 40 MPa with different fuel temperatures and back pressures. High-speed imaging was performed using an ultrahigh speed camera (1 million fps) coupled with a long-distance microscope. Under non-flash boiling conditions, the effect of Rs on fuel development was small but observable. Clear fuel collision can be observed at Rs=1.5 and 1.0. Under the flash boiling conditions, near-nozzle spray patterns were significant different from the non-flash boiling ones.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

Vehicle automation is a fundamental approach to reduce traffic accidents and driver workload. However, there is a notable risk of pushing human drivers out of the control loop before automation technology fully matures. Cooperative driving (or vehicle co-piloting) is a novel paradigm which is defined as the vehicle being jointly navigated by a human driver and an automatic controller through shared control technology. Indirect shared control is an emerging shared control method, which is able to realize cooperative driving through input complementation instead of haptic guidance. In this paper we first establish an indirect shared control method, in which the driver’s commanded input and the controller’s desired input are balanced with a weighted summation. Thereafter, we propose a predictive model to capture driver adaptation and trust in indirect shared control.

During the engaging process of sleeve and teeth ring in mechanical transmissions, their rotational speed and position differences cause multiple engaging ways and trajectories, and casual impacts between them will delay the engaging process and cause a long power off time for a gear shift. In order to reveal the engaging mechanism of the sleeve and the teeth ring, it is essential to build a high-fidelity model to cover all of their engaging ways and capture their speed changes for an impact. In this work, our contribution is that their impact process is modeled as a precise, continuous and nonlinear damping model, and then a hybrid automaton model is built to connect the system dynamics in different mechanical coupling relationships.

Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse.

As the restraint technologies for front-seat occupant protection advance, such as seatbelt pre-tensioner, seatbelt load limiter and airbag, relative effectiveness of rear-seat occupant protection decreases, especially for the elderly. Some occupant protection systems for front-seat have been proved to be effective for rear-seat occupant protection as well, but they also have some drawbacks. Seatbelt could generate unwanted local penetrations to the chest and abdomen. And for rear-seat occupants, it might be difficult to install airbag and set deployment time. For crash protection, it is desirable that the restraint loads are spread to the sturdy parts of human body such as head, shoulders, rib cage, pelvis and femurs, as uniformly as possible. This paper explores a uniform restraint concept aiming at providing protection in wide range of impact severity for rear-seat occupants.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

A new dynamic tire model for estimating the longitudinal/lateral road-tire friction force was derived in this paper. The model was based on the previous Dugoff tire model, in consideration of its drawback that it does not reflect the actual change trend that the tire friction force decreases with the increment of wheel slip ratio when it enters into the nonlinear region. The Dugoff model was modified by fitting a series of tire force data and compared with the commonly used Magic Formula model. This new dynamic friction model is able to capture accurately the transient behavior of the friction force observed during pure longitudinal wheel slip, lateral sideslip and combined slip situation. Simulation has been done under different situations, while the results validate the accuracy of the new tire friction model in predicting tire/road friction force during transient vehicle motion.

At first, the dynamic electromagnetic characteristics of a pulsed solenoid valve is analyzed by experiments. The fast valve response is obtained by material modifications. Then, the intelligent solenoid driving method is discussed. The new techniques of the “active” PWM and the “d2i/dt2” detection are developed for feedback control of the solenoid holding current and the valve closure timing. Finally, the control and diagnosis method for the valve closure duration is investigated. A sensing mechanism utilizing momentary camshaft speed fluctuations of fuel injection pump is presented, which provides the basis for feedback control and diagnosis of the valve closure duration and diesel fuel injection process.