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Gas distribution of proton exchange membrane fuel cells (PEMFCs) is mainly decided by flow field of bipolar plate. The improper design of distributing channel, nonuniform gas flow distribution and current density distribution among different straight channels are the leading factors that could tremendously undermine the performance and life expectancy of the cell. However, there is lack of research focusing on distributing channel in straight-parallel flow field. In this work, a three-dimensional numerical model of PEMFC cathode flow field is developed with CFD method to investigate the effects of configuration type and width of the distributing channel on pressure distribution in distributing channel and on reactant flow distribution, pressure drop and concentration distribution in multiple straight channel. Effects of electrochemical reaction and formation of water on the flow distribution are taken into consideration.

Based on the working model of a diesel engine, the influence of 2 Miller cycle strategies-Early Intake Valve Closure (EIVC) and Late Intake Valve Closure (LIVC) on the combustion and emissions of diesel engine was analyzed. Then the working condition of each Miller cycle strategies on the engine under the rated speed was optimized through the adjust of the valve timing, boost pressure and the injection timing. The research found that both delaying and advancing the closure timing of the intake valve can decrease the pressure and temperature during compression stroke, prolonging the ignition delay. However, due to the decrease of the working media inside the cylinder, the average in-cylinder temperature and soot emissions will increase, which can be alleviated by raising the boost pressure and the resulting compensation of the intake loss.

Recently, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure build-up and continuous pressure regulation. In contrast to the vacuum booster, the pressure estimation for EHB is worth to be studied due to its abundant resource (i.e. electric motor) and cost-effective benefit. This work improves an interconnected pressure estimation algorithm (IPEA) based on actuator characteristics by introducing the vehicle dynamics and validates it via vehicle tests. Considering the previous IPEA as the prior pressure estimation, the wheel speed feedback is used for modification via a proportional-integral (PI) observer. Superior to the IPEA based on actuator characteristics, the proposed PEA improves the accuracy by more than 20% under the mismatch of pressure-position relation.

Thin-walled structures have been widely used in automobile body design because of its good lightweight and superior mechanical properties. For the energy-absorbing box of the automobile, it is necessary to consider its working conditions under the axial and oblique impact. In this paper, a novel hierarchical honeycomb is proposed and used as filler for thin-walled structures. Meanwhile, the crashworthiness performances of the conventional honeycomb-filled and the hierarchical honeycomb-filled thin-walled structures under different impact conditions are systematically studied. The results indicate the energy absorption of the hierarchical honeycomb-filled thin-walled structure is higher than that of the conventional honeycomb-filled thin-walled structure, and the impact angle has significant effects on the energy absorption performance of the hierarchical honeycomb-filled structure.

In order to investigate the impacts of recirculated exhaust gas temperature on gasoline engine combustion and emissions, an experimental study has been conducted on a turbocharged PFI gasoline engine. The engine was equipped with a high pressure cooled EGR system, in which different EGR temperatures were realized by using different EGR coolants. The engine ran at 2000 r/min and 3000 r/min, and the BMEP varied from 0.2MPa to 1.0MPa with the step of 0.2MPa. At each case, there were three conditions: 0% EGR, 10% LT-EGR, 10% HT-EGR. The results indicated that LT-EGR had a longer combustion duration compared with HT-EGR. When BMEP was 1.0 MPa, CA50 of HT-EGR advanced about 5oCA. However, CA50 of LT-EGR could still keep steady and in appropriate range, which guaranteed good combustion efficiency. Besides, LT-EGR had lower exhaust gas temperature, which could help to suppress knock. And its lower exhaust gas temperature could reduce heat loss. These contributed to fuel consumption reduction.

The active engine mount (AEM) is developed in automotive industry to improve overall NVH performance. The AEM is designed to reduce major-order signals of engine vibration over a broad frequency range, therefore it is of vital importance to extract major-order signals from vibration before the actuator of the AEM works. This work focuses on a method of real-time extraction of the major-order acceleration signals at the passive side of the AEM. Firstly, the transient engine speed is tracked and calculated, from which the FFT method with a constant sampling rate is used to identify the time-related frequencies as the fundamental frequencies. Then the major-order signals in frequency domain are computed according to the certain multiple relation of the fundamental frequencies. After that, the major-order signals can be reconstructed in time domain, which are proved accurate through offline simulation, compared with the given signals.

Biodiesel as a renewable energy is becoming increasingly attractive due to the growing scarcity of conventional fossil fuels. Meanwhile, the development of after-treatment technologies for the diesel engine brings new insight concerning emissions especially the particulate matter pollutants. In order to study the coupling effects of biodiesel blend and CCRT (Catalyzed Continuously Regeneration Trap) on the particulate matter emissions, the particulate matter emissions from an urban bus with and without CCRT burning BD0 and BD10 respectively was tested and analyzed using electrical low pressure impactor (ELPI). The operation conditions included steady state conditions and transient conditions. Results showed that the particulate number-size distribution of BD10 and BD0 both had two peaks in nuclei mode and accumulation mode at the conditions of idle, low speed and medium speed while at high speed condition the particulate number-size distribution only had one peak.

Due to much higher pressure and pressure rising rate, knocking is always of potential hazards causing damages in the engine and high NOX emissions. Therefore, the researchers have focused on knocking diagnosis and control for many years. However, there is still lack of fast response sensor detecting in-cycle knocking. Until now, the feedback control based on knocking sensor normally adjusts the injection and ignition parameters of the following cycles after knocking appears. Thus in-cycle knocking feedback control which requires a predictive combustion signal is still hard to see. Ion current signal is feasible for real-time in-cylinder combustion detection, and can be employed for misfiring and knocking detection. Based on incylinder pressure and ion current signals, the in-cycle knocking feedback control is investigated in this research. The 2nd-order differential of in-cylinder pressure, which means the response time of pressure rising rate dPR, is employed for knocking prediction.

With regulatory standards for diesel engine emissions becoming stricter worldwide, integrated catalytic systems are becoming increasingly necessary. One of the better approaches is to use an after-treatment system consisting of a diesel oxidation catalyst (DOC), a catalyzed diesel particulate filter (CDPF), and a selective catalytic reduction (SCR), but many factors can affect how well this system works. This study investigates the effects of DOC and CDPF catalyst composition on emissions characteristics for DOC + CDPF + SCR systems by collecting reactor and engine data. The reactor results show that the light-off temperatures (T50) of CO and C3H6 increase with the growth of Pt:Pd ratio while the T50 of NO degrades. An engine dynamometer test was conducted on a light-duty diesel engine equipped with DOC + CDPF + SCR. The results show light-off curves of CO and THC that are smoother than the reactor data.

The flow issues of the turbocharged intake system for a diesel engine are mainly introduced in this work and the effects of a multi-chamber perforated resonator which can efficiently attenuate broadband noise and has compact structure on the flow performances of the intake system is analyzed by contrast. Based on the acoustic grid resulting from pre-processing of 3D models for finite element analysis, a computational fluid dynamics flow simulation comparative analysis between the intake systems with and without a resonator including pressure and velocity distribution is conducted with the software Star-CCM+. The simulation results indicate that the air pressure drop of the intake system with a resonator is slightly higher than that of the intake system without a resonator but it is still relatively low compared with that of the entire intake system.

With the development of autonomous driving, the penetration rate of autonomous vehicles on the road will continue to grow. As a result, the social cooperation ability of autonomous vehicles will have a great effect on the social acceptance of autonomous driving, which can be described as harmony with traffic. In order to research the evaluation method of the harmony with traffic, this paper proposes a subjective and objective mapping evaluation method based on the Mean Impact Value and Backpropagation (MIV-BP) Neural Network, with the merging vehicle on the expressway ramp as the research object. Firstly, by taking 16 original objective indexes obtained by theoretical analysis and the subjective evaluation results as input and output, respectively, the BP Neural Network model is constructed as a baseline model. Secondly, nine selected objective indexes are selected by the MIV method based on the baseline model.

Most of the existing energy management strategies for Extended-Range Electric Vehicles (E-REVs) are heuristic, which restricts coordination between the battery and the Range Extender. This paper presents an instantaneous optimization energy management strategy based on the Minimum Loss Power Algorithm (MLPA) for a fuel cell E-REV. An instantaneous loss power function of power train system is constructed by considering the charge and discharge efficiency of the battery, together with the working efficiency of the fuel cell Range Extender. The battery working mode and operating points of the fuel cell Range Extender are decided by an instantaneous optimization module (an artificial neural network) that aims to minimize the loss power function at each time step.

BBW (Brake-by-wire) can increase the vehicle safety performance due to high control accuracy and fast response speed. As one solution of BBW, the novel Integrated-electro-hydraulic brake system (I-EHB) is proposed, which consists of electro-hydraulic booster and hydraulic pressure control unit. The electro-hydraulic booster is activated by an electric motor that driving linear motion mechanism to directly produce the master cylinder pressure. With electro-hydraulic booster as an actuator, the hydraulic pressure control problem is a key issue. Most literatures deal with the pressure control issue based on the feedback pressure signal measured by pressure sensor. As far as the authors are aware, none of the proposed techniques takes into account the pressure sensor unequipped BBW. In this paper, there is no pressure feedback signal, but there is only position feedback signal measured by position sensor for control law design.

The accelerated global progress in the research and development of automobile products, and the use of new technologies, such as the Internet, cloud computing and big data, to coordinate development platforms in different regions and fields, can reduce the duration and cost of development and testing. Specifically, in the context of the current coronavirus disease (COVID-19) pandemic, which has caused great obstacles to normal logistics and transportation, personnel exchanges and information communication, platforms that can support global operation are significant for product testing and validation, because they eliminate the need for the transportation of personnel and equipment. Therefore, the establishment of a distributed test and validation platform for automotive powertrain systems, which can integrate software and hardware testing, is important in terms of both scientific research and industrialization.

The prediction of passenger flow is of great significance to facilitate the decision-making processes for local authorities and transport operators to provide an effective bus scheduling. In this work, a backpropagation neural network (BPNN) was adopted to predict the bus passenger flow. To reduce the prediction error and improve the prediction accuracy, a combined model CEEMDAN-BP, which combines CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) method and BPNN, has been proposed. CEEMDAN is an improved method based on EEMD, which has been widely applied to signal smoothing and de-noising. Experimental results show that this combined model can exactly achieve an excellent prediction effect and improve the prediction accuracy of the network greatly.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve).

As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

Ethanol reforming gas combined with EGR technology can not only improve thermal efficiency, but also reduce pollutant emission under lean combustion condition. In this investigation, GT-Power is used to carry out one-dimensional simulation model calculation and analysis to explore the combustion characteristics, economy performance of a direct injection gasoline engine when the excess air coefficient (λ) increases from 1 to 1.3 and the ethanol reforming gas mixing ratio increases from 0% to 30% at the working condition of 2000 r/min and 10 bar. Then the EGR system is introduced to deeply discuss the working characteristics of the direct injection gasoline engine when the EGR rate increases from 0% to 20%. The results show that the increase of λ leads to the decrease of in-cylinder pressure and the delay of the peak of cylinder pressure.

Optimization design of hard point parameters for hinge mechanism has been paid more attention in recent years, attributable to their significant improvement in dynamic performance. In this paper, the experimental analysis and dynamic optimization design of hinge mechanism is performed. The acceleration measurement experiments are carried out at different arrangement points and under different working conditions. Furthermore, the accuracy of established multi-body dynamics model is verified by three-axis accelerometer measurement experiment. In addition, sensitivity analysis for electric strut and gas strut coordinates is performed and shows that the Y coordinate of the lower end point of the electric strut is the design variable that has the greatest impact on the responses.