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Fuel cell reactors, as the core components of fuel cell vehicles, have a short life problem that has always limited the development of fuel cell vehicles. The life attenuation curve of fuel cell shows nonlinear characteristics, and there is no model that can accurately predict its effect. This paper is based on the experimental data of the vehicle fuel cell reactor, which is derived from the 600 h durability test run by a 4 kW fuel cell reactor. The relevance vector machine, as a Bayes processing method that supports vector machine, is a data-driven method based on kernel functions. The regression model is established by the relevance vector machine, and the super-parameters are found by genetic algorithm, because the kernel function strongly affects the nonlinearity of the curve, and the decay curve of fuel cell reactor performance is predicted according to four different kernel functions.

In this work, the influences of various real-timely available energy management strategies on vehicle fuel consumption (VFC) and energy flow of a range-extender electric vehicle were studied The strategies include single-point, multi-point, speed-following, and equivalent consumption minimization strategy. In addition, the dynamic programming method which cannot be used in real time, but can provide the optimal solution for a known drive situation was used for comparison. VFCs and energy flow characteristics with different strategies under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) were obtained through computer modeling, and the results were verified experimentally on a range-extender test bench. The experimental results are consistent with the modeled ones in general with a maximum deviation of 4.11%, which verifies the accuracy of the simulation models.

The purpose of present numerical study was to extend the operating range of alcohol (methanol and ethanol) fueled Homogeneous Charge Compression Ignition (HCCI) engine under low load conditions. Ignition of pure methanol and ethanol under HCCI mode of operation requires high intake temperatures and misfires at low loads are common in HCCI engines. Three methods have been adapted to optimize the use of methanol and ethanol for HCCI operation without increasing the intake temperature. First, blending methanol and ethanol with ignition improver, namely di-methyl ether (DME) and di-ethyl ether (DEE), was used to increase the cetane number and ignitability of premixed charge. Second, based on the blended fuels, the spark assistance was used to reduce required intake temperature for auto-ignition. Third, DME and DEE were directly injected to methanol and ethanol operated HCCI engine, in the form of Reactivity Controlled Compression Ignition (RCCI) combustion.

Life prediction is a major focus for a commercial fuel cell stack, especially applied in fuel cell electric vehicles (FCEV). This paper proposes a data driven fuel cell lifetime prediction model using particle swarm optimized back-propagation neural network (PSO-BPNN). For the prediction model PSO-BP, PSO algorithm is used to determine the optimal hyper parameters of BP neural network. In this paper, total voltage of fuel cell stack is employed to represent the health index of fuel cell. Then the proposed prediction model is validated by the aging data from PEMFC stack in FCEV at the actual road condition. The experimental results indicate that PSO-BP model can predict the voltage degradation of PEMFC stack at actual road condition precisely and has a higher prediction accuracy than BP model.

In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

Gas purge is considered as an essential shutdown process for a PEMFC (Proton Exchange Membrane Fuel Cell), especially in subfreezing temperature. The water flooding phenomenon inside fuel cell flow channel have a marked impact on performance in normal operating condition. In addition, the residual water freezes in the subzero temperature, thus blocking the mass transfer from flow channel to porous media. Therefore, the gas purge course is of primary importance for improvement of performance and durability. The water droplet residing in the flow channel can be purged out due to shearing force of gas. In fact, the flow channel is not completely flat due to surface roughness of gas diffusion layer (GDL), meaning the water droplet may climb over obstacles. Moreover, the water droplet may block the flow channel and then be sheared into films on the surface of GDL.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

Dry dual clutch transmission (DCT) has played an important role in the high performance applications as well as low-cost market sectors in Asia, with a potential as the future mainstream transmission technology due to its high mechanical efficiency and driving comfort. Control system simplification and cost reduction has been critical in making dry DCT more competitive against other transmission technologies. Specifically, DCT clutch actuation system is a key component with a great potential for cost-saving as well as performance improvement. In this paper, a new motor driven clutch actuator with a force-aid lever has been proposed. A spring is added to assist clutch apply that can effectively reduce the motor size and energy consumption. The goal of this paper is to investigate the feasibility of this new clutch actuator, and the force-aid lever actuator's principle, physical structure design, and validation results are discussed in details.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

As a critical technology for autonomous vehicles, high precise positioning is essential for automated container terminals to implement intelligent dispatching and to improve container transport efficiency. Because of the unstable performance of global positioning system (GPS) in some circumstances, an ultra wide band (UWB) positioning system is developed for autonomous trucks in an automated container terminal. In this paper, an asynchronous structure is adopted in the system, and a three-dimensional (3D) localization method is proposed. Other than a traditional UWB positioning system with a server, in this asynchronous system, positions are calculated in the vehicle. Therefore, propagation delays from the server to vehicles are eliminated, and the real-time performance can be significantly improved. Traditional 3D localization methods based on time difference of arrival (TDOA) are mostly invalid with anchors in the same plane.

To study the state of health (SOH) of the proton exchange membrane fuel cell (PEMFC), a novel hybrid method combining the advantages of both the model-based and data-driven methods is proposed. Firstly, the model-based method is proposed based on the voltage degradation model to estimate the variation trend, and three parameters reflecting the performance degradation are selected. Secondly, the data-driven (long short-term memory (LSTM)) method is presented to estimate the variation fluctuation. Moreover, the core step of the hybrid method is returning the results of the LSTM method to the power degradation model as the “observation” and modifying related parameters to improve the estimation accuracy. Finally, the sliding window method is applied to solve the problem of the data increase with the increase of the operating time. The results show that the power estimation is better than the current estimation for the SOH estimation.

Autonomous driving related technologies have become a hot topic in academia and industry. Planning control is one of the core technologies of autonomous driving, which is conducive to vehicles safe and efficient driving. This paper proposes a novel optimal speed control algorithm, which considers the power system's energy consumption, the speed limit on the road, and the safe distance of the vehicle in front. An optimal speed control model of “From battery to wheel” energy consumption is established by constructing a performance index function based on the best-fitting formula of motor power, motor speed and torque. Based on the optimal control principle, the fourth-order ordinary differential equation of the speed control model is established, based on the indirect adjoining approach, the speed control model under the restriction of the road speed limit and safe distance of the preceding vehicle is derived and the analytical expression is obtained.

Cold start capability is one of remaining major challenges in realizing PEMFC (Proton Exchange Membrane Fuel Cell) technology for automotive applications. Gas purge is a common and integral shutdown procedure of a PEMFC automotive in subzero temperature. A dryer membrane electrode assembly (MEA) can store more water before it gets saturated and ice starts to penetrate in the open pores of porous media, thus enhancing cold start capability of a PEMFC. Therefore, gas purge is always performed prior to fuel cell shutdown to minimize residual water in a PEMFC. In the hope of improving effectiveness of purge in a PEMFC vehicle, two important purge parameters are evaluated including purge time and energy requirement. In practice, an optimized gas purge protocol should be developed with minimal parasitic energy, short purge duration and no degradation of components. To conclude, the cold start capability and performance can be consolidated by proper design of gas purge strategies.

Fuel cell vehicles (FCV) have become a promising transportation tool because of their high efficiency, fast response and zero-emission. However, the cold start problem is one of the main obstacles to limit the further commercialization of FCV in cold weather countries. Many efforts have made to improve the cold start ability. This review presents comprehensive heating methods and influence factors of the research progress in solving the Proton Exchange Membrane Fuel Cells (PEMFC) system cold start problems with more than 100 patents, papers and reports, which may do some help for PEMFC system cold start from the point of practical utilization. Firstly, recent achievements and goals will be summarized in the introduction part. Then, regarding the heating strategies for the PEMFC system cold start, different heating solutions are classified into self-heating strategies and auxiliary-heating heating depending on their heating sources providing approach.

Parallel-connected modules have been widely used in battery packs for electric vehicles nowadays. Unlike series-connected modules, the direct state inconsistency caused by parameter inconsistency in parallel modules is current and temperature non-uniformity, thus resulting in the inconsistency in the speed of aging among cells. Consequently, the evolution pattern of parameter inconsistency is different from that of series-connected modules. Since it’s practically impossible to monitor each cell’s current and temperature information in battery packs, considering cost and energy efficiency, it’s necessary to study how the parameter inconsistency evolves in parallel modules considering the initial parameter distribution, topology design and working condition. In this study, we assigned cells of 18650 format into several groups regarding the degree of capacity and resistance inconsistency. Then all groups are cycled under different environmental temperature and current profile.

The hydrogen supply integrated subsystem is an important part of the proton exchange membrane fuel cell system. In the multi-stack fuel cell system, the optimal design and integration of the hydrogen supply subsystem have great influence on the whole system structure. In this paper, a fuel cell hydrogen integration subsystem with two hydrogen cycle structures is established based on an optimized split-stack approach. Firstly, the matching of hydrogen subsystem is carried out on the basis of multi-stack fuel cell optimization. Then, the structure of the gas buffering and distribution device and the gas circulation device is optimized considering the gas circulation and the diversity of the equipment, and two solutions are proposed: the separate circulation structure (Structure I) and the common circulation structure (Structure II). Finally, the multi-stack fuel cell system is built by MATLAB/Simulink software and simulated under the condition of step and C-WTVC.

The optimization of speed holds critical significance for pure electric vehicles. In multi-intersection scenarios, the determination of terminal velocity plays a crucial role in addressing the complexities of the speed optimization problem. However, prevailing methodologies documented in the literature predominantly adhere to a fixed speed constraint derived from traffic light regulations, serving as the primary basis for the terminal velocity constraint. Nevertheless, this strategy can result in unnecessary acceleration and deceleration maneuvers, consequently leading to an undesirable escalation in energy consumption. To mitigate these issues and attain an optimal terminal velocity, this paper proposes an innovative speed optimization method that incorporates a terminal-velocity heuristic. Firstly, a traffic light state model is established to determine the speed range required to avoid coming to a stop at signalized intersections.

Gesture control has been increasingly applied to automotive industry to reduce the distraction caused by in-vehicle interactions to the primary task of driving. The aim of this study is to find out if gestures can reasonably be used to control in-car devices. Since there exists a big cultural difference of gesture between different countries because of its particularity, a set of gestures which support intuitive human-machine interaction in an automotive environment is searched. The results show a gesture dictionary for a variety of on-board functions, which conforms to Chinese drivers’ driving habits. Furthermore, this paper also describes a driving simulator test to evaluate the usability of gesture from different aspects including the effectiveness, efficiency, satisfaction, memorability and security. Static driving simulator is considered as an excellent environment for the in-car secondary task as its high safety level, repeatability and reliability.