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Due to the rapid development of road infrastructure and vehicle population in China, the fuel consumption and emission of on-road vehicles tested in China World Transient Vehicle Cycle (C-WTVC) cannot indicate the real driving results. But the test results in China Heavy-duty Commercial Vehicle Test Cycle-Coach (CHTC-C) based on the road driving conditions in China are closer to the actual driving data. In this paper, the model for predicting the performance of heavy-duty vehicles is established and validated. The fuel consumption and NOx emission of a Euro VI heavy-duty coach under C-WTVC and CHTC-C tests are calculated by employing the developed model. Furthermore, the fuel consumption of the test coach is optimized and its sensitivity to the driving factors is analyzed.

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.

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.

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).

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.

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.

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.

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.

Advanced compression ignition combustion system which reduces simultaneously both nitride oxides (NOx) and particulate matter (PM) is a promising approach to meet future emission regulations. In order to achieve advanced compression ignition, flexible fuel injection is required for ultra-early and post-TDC injections, which conventional injector fails to accomplish due to wall-wetting effect. In this work, special injectors with the spray angle of 60 degree are applied on a 4 cylinder mass-production diesel engine without modification of the engine configuration. For application-oriented study, sweep experiments of injection timings and durations, fuel injection pressure and the boost pressure are carried out to investigate the relationships between the control parameters and the engine performance. Model based calibration and real application tests validate the maximum applicable operation range of maximum speed of 2200 RPM and IMEP of 8.0 bar.

Battery is a vital part of a fuel cell hybrid vehicle, and also the most difficult part to model due to its nonlinearity. Therefore, This paper presents an integrated software-hardware solution to simulate the fuel cell vehicle power train more accurately based on battery-in-the-loop, with the aid of RT-LAB™. Moreover, the average modeling technique is used together with RT-LAB's distributed cluster technology to realize real-time simulation of the Field-Oriented Controlled induction motor drive, and the Boost DC/DC converter. As a result, a virtual test bed, which is very similar to actual power train, is set up. Finally, on this test bed some tests are performed to verify the existing battery model and soc estimation method, and to give more accurate fuel consumption results.

Neutral-idle strategy has been applied for years to improve the fuel consumption of automatic transmission cars. The updated demand is the use of expanded slipping control strategy for further improvement of the transmission efficiency and response speed. However, one major drawback of the continuous slipping clutches is the high tendency to produce shudder or low frequency variation. In this research, a special neutral-idle shudder phenomenon is presented. This special shudder is not only related to slipping clutches but also related to the vibration and structure of the powertrain system. Simulations and experiments are conducted to give an insight view of this phenomenon. The analysis reveals that this special shudder is caused by both torsional vibration of the driveline and rigid-body vibration of the powertrain system. A positive feedback loop between those two kinds of vibrations leads to this special neutral-idle shudder.

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.

Gasoline direct injection (GDI) engines have been developed rapidly in recent years, driven by stringent legislative requirements on vehicle fuel efficiency and emissions. However, one challenge facing GDI is the formation of particulate emissions, particularly with the presence of injector tip deposits. The Chinese market features some gasoline fuels that contain no detergent additives and are prone to deposit formation, which can affect engine performance and emissions. The use of detergent additives to mitigate the formation of injector deposits in a GDI engine was investigated in this study by testing a 1.5L turbocharged GDI engine available in the Chinese market. The engine was operated both on base gasoline and on gasoline dosed with detergent additives to evaluate the effect on injector deposit formation and engine performance and emissions.

Energy saving is becoming one of the most important issues for the next generation of commercial vehicles. The fuel consumption limits for commercial vehicles in China have stepped into the third stage, which is a great challenge for heavy duty commercial vehicles. Hybrid technology provides a promising method to solve this problem, of which the dual motor coaxial series parallel configuration is one of the best options. Compared with parallel configuration, the powertrain can not only operate in pure electric or parallel mode, but also can operate in series mode, which shows better flexibility. In this paper, regulations on test cycle, fuel consumption limits and calculation method of the third stage will be introduced in detail. Then, the quasi-static models of the coaxial series parallel powertrain with/without gearbox under C-WTVC (China worldwide transient vehicle cycle) are built. The control strategies are designed based on engine and motor performance.

Power-split configuration is highlighted as the most popular concept for full hybrid electric vehicles (HEV). However, the energy management and design of power-split heavy duty truck under Chinese driving conditions still need to be investigated. In this paper, the parametric design, a rule-based control strategy and an equivalent consumption minimization strategy (ECMS) for the power-split heavy duty truck are presented. Besides, the influence of a penalty factor also discussed under ECMS algorithm. Meanwhile, two different methods to search the engine operation point have been proposed and the reason of different economy performance is presented by using energy flow chart. And the simulation results show both fuel consumption can satisfy the second phase fuel consumption standard and the third phase fuel consumption standard which will be implemented in 2020, under C-WTVC (Chinese-World Transient Vehicle Cycle).

This research focuses on the potential of Homogeneous Charge Induced Ignition (HCII) combustion meeting the Euro V emission standard on a heavy-duty multi-cylinder engine using a simple after-treatment system. However, in our previous studies, it was found that the gasoline ratio was limited in HCII by the over-high compression ratio (CR). In this paper, the effects of reducing CR on the performances of HCII at medium and high loads were explored by experimental methods. It was found that by reducing CR from 18:1 to 16:1 the peak in-cylinder pressure and the peak pressure rise rate were effectively reduced and the gasoline ratio range could be obviously extended. Thus, the combustion and emission characteristics of HCII at medium and high loads were noticeably improved. Soot emissions can be significantly reduced because of the increase of premixed combustion ratio. The reduction could be over 50% especially at high load and high speed conditions.

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.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.