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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.

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).

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.

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%.

An integrated simulation platform for turbocharged internal combustion engines has been developed. Multi-dimensional computational fluid dynamic (CFD) codes are integrated into the system to model the turbocharging circuit, gas circuit, in-cylinder circuit, coolant and oil circuits. As the turbocharger is a critical factor for the IC engine, a turbocharger through-flow model based on mass, momentum, and energy conservation equations has been developed and added in the integrated platform. Compared with the traditional MAP method, the through-flow model can solve the problems of transient matching and lack of numerous experimental maps during the pre-prototype engine design. Partial systems in the integrated platform, such as the in-cylinder flow and combustion circuit, can be modeled by 3-D CFD codes for the investigation of the detailed flow patterns.

Clarifying accident causations can provide a strong foundation to prevent traffic accidents and reduce severities. This paper uses Chinese government census data from 1996-2003[1∼8] and models a relationship between various kinds of traffic accident causations and the severities of the traffic accidents based on neural network combined (NNC). The paper adapts multi-folder cross validation concept to enhance the properties of NNC. It then conducts sensitivity analysis on the trained NNC to identify the prioritized importance of traffic accident causations as they are to the severities of traffic accident. Lastly, the results are validated and compared by the findings of previous researches.

The large amount of controllable fuel injection parameters of Diesel engine equipped with high pressure common-rail fuel injection system makes the control of combustion more flexible, and also makes the workload of calibration and optimization much heavier. For higher efficiency, model-based approaches are presented and researched. This contribution presents a new method for modeling which is constituted by Neural Network and Adaptive Network-based Fussy Inference System (ANFIS). The experiment is carried out on a 6-cylinder common rail diesel engine. The analysis and experiment show that effective modeling can be achieved using this method.

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.

Hydraulic retarder is important auxiliary brake device which widely used in commercial vehicles for its economy, safety and driving comfort, however cavitation will occur and reduce the braking performance when hydraulic retarder operates at high speed. In this paper, a model of hydraulic retarder considering cavitation effect was established, and the reliability of the model was verified by comparing with the external characteristics of the product which was obtained from Voith’s official discloses data. Then the cavitation of hydraulic retarder under high-speed working condition was studied by the establishing simulation model. The simulation model can describe and analyze the internal flow field in the hydraulic retarder, and can be used as an important tool for the development and optimization of hydraulic retarder in the future. When hydraulic retarder’s rotational speed is about 1500rpm, the cavitation will be observed in the working chamber.

To further explore the potential of fuel economy for hybrid electric vehicle (HEV), a methodology of demand power optimization is proposed. The fuel consumption depends not only on the EMS, but also on the way to operate vehicle. A control strategy to adjust driver’s demand before power splitting is necessary. To get accurate and reliable control strategy, two aspects are the most important. First, a rigorous and organized modeling approach is a base to describe complicated powertrain system of HEV. The energetic macroscopic representation (EMR) is a graphical synthetic description of electromechanical conversion system based on energy flow. A powertrain architecture of HEV is described explicitly via the EMR. Second, the effectiveness of EMS and the reasonability of driving operations are vital.

With the development of intelligent vehicle and active vehicle safety systems, the demand of sensors is increasing, especially in-tire sensors. Tire parameters are essential for vehicle dynamic control, including tire pressure, tire temperature, slip angle, longitudinal force, etc.. The diversification and growth of in-tire sensors require adequate power supply. Traditionally, embedded batteries are used to power sensors in tire, however, they must be replaced periodically because of the limited energy storage. The power limitation of the batteries would reduce the real-time data transmission frequency and deteriorate the vehicle safety. Heightened interest focuses on generating power through energy harvesting systems in replace of the batteries. Current in-tire energy harvesting devices include piezoelectric, electromagnetic, electrostatic and electromechanical mechanism, whose energy sources include tire deformations, vibrations and rotations.

The desired yaw rate is a vital target parameter for vehicle stability control, which is currently determined as a steady-state yaw rate by the linear single-track vehicle model. Tire nonlinearity deteriorates the effect of vehicle stability control at larger lateral acceleration. This paper proposes a new calculation method of the steady-state yaw rate considering the tire nonlinearity based on the brush tire model. To validate and verify the proposed method, step steering tests of the target vehicle under different lateral accelerations are carried out on a real proving ground. The results show that when the lateral acceleration is relatively small, the difference between the calculation results of the proposed method and the traditional one is not apparent, and both methods can provide a good estimation for the steady-state yaw rate; however, when the lateral acceleration is relatively large, the difference becomes apparent.

Batteries are exposed to dynamic load during vehicle driving. It is significant to clarify the load input of the battery system during vehicle driving for battery pack structural design and optimization. Currently, bolt connection is mostly applied for battery pack constraint to vehicle, as well as for module assembly inside the pack. However, accurate bolt load is always difficult to obtain, while directly force measurement is expensive and time consuming in engineering. In this paper, a precise data driven model based on Elman neural network is established to identify the dynamic bolt loads of the battery pack, using tested acceleration data near bolts. The dynamic bolt force data is measured at the same time with the acceleration data during vehicle running in different driving conditions, utilizing customized bolt force sensors.