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Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, we discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model.

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).

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.

Acetone-Butanol-Ethanol (ABE), an intermediate product in the ABE fermentation process for producing bio-butanol, is considered a promising alternative fuel because it not only preserves the advantages of oxygenated fuel which typically emit less pollutants compared to conventional diesel, but also lowers the cost of fuel recovery for each individual component during the fermentation. With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. In this respect, it is desirable to estimate the performance of different ABE blends to determine the best blend and optimize the production process accordingly. ABE fuels with different component ratio, (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %), were blended with diesel and tested in a constant volume chamber.

Polyoxymethylene dimethyl ethers (PODEn) have high cetane number, high oxygen content and high volatility, therefore can be added to gasoline to optimize the performance and soot emission of Gasoline Compression Ignition (GCI) combustion. High speed imaging was used to investigate the spray and combustion process of gasoline/PODEn blends (PODEn volume fraction 0%-30%) under various ambient conditions and injection strategies in a constant volume vessel. Results showed that with an increase of PODEn proportion from 10% to 30%, liquid-phase penetration of the spray increased slightly, ignition delay decreased from 3.8 ms to 2.0 ms and flame lift off length decreased 29.4%, causing a significant increase of the flame luminance. For blends with 20% PODEn, when ambient temperature decreased from 893 K to 823 K, the ignition delay increased 1.3 ms and the flame luminance got lower.

With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state).

Recent research has shown that butanol, instead of ethanol, has the potential of introducing a more suitable blend in diesel engines. This is because butanol has properties similar to current transportation fuels in comparison to ethanol. However, the main downside is the high cost of the butanol production process. Acetone-butanol-ethanol (ABE) is an intermediate product of the fermentation process of butanol production. By eliminating the separation and purification processes, using ABE directly in diesel blends has the potential of greatly decreasing the overall cost for fuel production. This could lead to a vast commercial use of ABE-diesel blends on the market. Much research has been done in the past five years concerning spray and combustion processes of both neat ABE and ABE-diesel mixtures. Additionally, different compositions of ABE mixtures had been characterized with a similar experimental approach.

Wide Distillation Fuel (WDF), with a distillation range from Initial Boiling Point of gasoline to Final Boiling Point of diesel, can be easily gained directly by blending diesel with gasoline. However, the reduced auto-ignitability of WDF could lead to higher HC emissions. Polyoxymethylene Dimethyl Ethers (PODE), with good volatility and oxygen content of up to 49%, have great potential to improve combustion and emission characteristics, especially for soot reduction. Experiments were carried out in a light-duty four-cylinder diesel engine fueled with neat diesel, gasoline/diesel blends (GD), GD/PODE blends (GDP) and the combustion and emission characteristics were carefully examined. Results showed that GDP had the lowest PM emission and diesel had the poorest one among the three fuels. Due to the addition of gasoline and the relatively poor ignitability, GD had lower combustion efficiency and higher Soluble Organic Fraction (SOF) emissions than diesel.

An air-cooled, four-stroke, 125 cc electronic gasoline fuel injection SI engine for motorcycles is altered to burn ethanol fuel. The effects of nozzle orifice size, fuel injection duration, spark timing and the excess air/ fuel ratio on engine power output, fuel and energy consumptions and engine exhaust emission levels are studied on an engine test bed. The results show that the maximum engine power output is increased by 5.4% and the maximum torque output is increased by 1.9% with the ethanol fuel in comparison with the baseline. At full load and 7000 r/min, HC emission is decreased by 38% and CO emission is decreased 46% on average over the whole engine speed range. However, NOx levels are increased to meet the maximum power output. The experiments of the spark timing show that the levels of HC and NOx emission are decreased markedly by the delay of spark timing.

Using Miller cycle is one of the effective ways to improve the SI engine efficiency and reduce CO₂ emissions. While much information is in the literature about the research on Miller cycle for gasoline engines, very limited experimental data have been published with respect Miller cycle when the engine is fueled by bio-fuels and this has been considered in the present study of 2,5-dimethylfuran (DMF) which is a new promising biofuel candidate. In this research, a single-cylinder naturally aspirated direct-injection spark-ignition (DISI) engine was modified to operate under the Miller cycle condition by using late inlet valve closing strategy. The engine tests were conducted with a compression ratio of 11.5 at the engine speed of 1500 rpm for three different fuels, gasoline, DMF and bio-ethanol. The effect of fuel properties on the performance and emissions of the engine was examined.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Polyoxymethylene Dimethyl Ether (PODEn) is a promising green additive to diesel fuel, owing to the unique chemical structure (CH3O[CH2O]nCH3, n≥2) and high cetane number. Together with the general wide-distillation fuel (WDF), which has an attractive potential to reduce the cost of production of vehicle fuel, the oxygenated WDF with PODEn can help achieve a high efficiency and low emissions of soot, NOx, HC, and CO simultaneously. In this paper, the first detailed reaction mechanism (225 species, 1082 reactions) which can describe the ignition characteristics of PODE1 and PODE3 at low temperature was developed.

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.

The ethanol has potential to be a renewable alternative fuel for internal combustion engines and contributes to lower global CO2 emission. In this study, vegetable methyl ester is added in the ethanol-diesel fuel to prevent separation of the ethanol from diesel, thus the ethanol blend ratio can be set up to 30% in volume. This work pays more attention on its spray, effects of the ethanol percentage on the detailed PM components. To investigate the spray behavior of ethanol, diesel and their blends, experiments in a constant volume chamber were carried out combining numerical simulation. Properties of the ethanol-diesel blended fuels were obtained through some measurements and empirical calculations. The breakup sub-model, Wave-KH model considering the blend fuel properties were adopted in an engine simulation code KIVA-3V. The simulation had a good agreement with experiments.

Ethanol fuel blends with gasoline for spark ignition (SI) internal combustion engines are widely used on account of their advantages in terms of fuel economy and emissions reduction potential. The focus of this paper is to study the effects of these blends on combustion characteristics such as in-cylinder pressure profiles, gas-phase emissions (e.g., unburned hydrocarbons, NOx) and particulates (e.g., particulate matter and particle number) using both measurement campaigns and digital engineering workflows. Nineteen load-speed operating points in a 1L 3-cylinder GDI SI engine were measured and modelled. The measurements for in-cylinder pressure and emissions were repeated at each operating point for three types of fuel: gasoline (E0, 0% by volume of ethanol blend), E10 (10 % by volume of ethanol blend) and E20 (20% by volume of ethanol blend).

In this study, an experimental investigation was conducted using a direct injection single-cylinder diesel engine equipped with a test common rail fuel injection system to clarify how dimethyl ether (DME) injection characteristics affect the heat release and exhaust emissions. For that purpose the common rail fuel injection system (injection pressure: 15 MPa) and injection nozzle (0.55 × 5-holes, 0.70 × 3-holes, same total holes area) have been used for the test. First, to characterize the effect of DME physical properties on the macroscopic spray behavior: injection quantity, injection rate, penetration, cone angle, volume were measured using high-pressure injection chamber (pressure: 4MPa). In order to clarify effects of the injection process on HC, CO, and NOx emissions, as well as the rate of heat release were investigated by single-cylinder engine test. The effects of the injection rate and swirl ratio on exhaust emissions and heat release were also investigated.

Octane number (ON) and octane sensitivity (S), the fuel anti-knock indices, are critical for the design of advanced jet ignition engines. In this study, ten fuels with different research octane number (RON) and varying S were formulated based on ethanol reference fuels (ERFs) to investigate the effect of S on combustion of jet ignition engine. To fully understand S effects, the combustion characteristics under EGR dilution and lean burn were further investigated. The results indicated that increasing S resulted in higher reactivity with shorter ignition delay and combustion duration. The increase of reactivity led to heavier knocking intensity. The competition between the flame speed and the reactivity of the mixture determined the auto-ignition fraction of mixture and the knocking onset crank angle as S varied. Medium S (S=3) was helpful to improve the combustion speed, reduce the auto-ignition fraction of mixture and retard the knocking onset crank angle.

Fuel quality has a significant influence on the combustion engine operation. In recent years the increasing concerns about environmental protection, energy saving, energy security and the requirements of protecting fuel injection and aftertreatment systems have been major driving forces for the Chinese fuel specification evolution. The major property changes in the evolution of Chinese national gasoline and diesel standards are introduced and the reasons behind these changes are analyzed in this paper. The gasoline fuel development from State I to State VI-B involved a decrease of sulfur, manganese, olefins, aromatics and benzene content. The diesel fuel quality improvement from State I to State VI included achieving low sulfur fuels and a cetane number (CN) increase. Provincial fuel standards, stricter than corresponding national standards, were implemented in economically developed areas in the past.

Ammonia and methanol are both future fuels with carbon-neutral potential. Ammonia has a high octane number, a slow flame speed, and a narrow ignition limit, while methanol has a fast flame speed with complementary combustion characteristics but is more likely to lead to pre-ignition and knock. In this paper, the combustion and emission characteristics of ammonia-methanol solution in a high compression ratio spark ignition engine are investigated. The experimental results show that the peak in-cylinder pressure and peak heat release rate of the engine when using ammonia-methanol solution are lower and the combustion phase is retarded compared with using methanol at the same spark timing conditions. Using ammonia-methanol solution in the engine resulted in a more ideal combustion phase than that of gasoline, leading to an increase in indicated thermal efficiency of more than 0.6% and a wider range of efficient operating conditions.

Accurate identification of driver’s braking intention is essential in advanced driver assistance system and can make the driving process more comfortable and trustworthy. In this paper, a novel method for driver braking intention identification in cut-in scenarios was proposed by using driver’s gaze information and motion information of cut-in vehicles. Firstly, a "looking in and looking out" experimental platform including three eye-tracking cameras and one front-view camera was built to collect driver's gaze information and the vehicle motion information. Secondly, driver’s gaze features and motion features of cut-in vehicles were selected and the braking intention identification performance of several decision tree-based ensemble learning algorithms was compared. Thirdly, the feature importance was analyzed by using SHAP (SHapley Additive exPlanations) values. This novel method of braking intention identification makes full use of in-vehicle camera sensors.