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The combustion of dual-fuel engines usually uses a pilot flame to burn out a background fuel inside a cylinder under high compression. The background fuel can be either a gaseous fuel or a volatile liquid fuel, commonly with low reactivity to prevent premature combustion and engine knocking; whereas the pilot flame is normally set off with the direct injection of a liquid fuel with adequate reactivity that is suitable for deterministic auto-ignition with a high compression ratio. In this work, directly injected butanol is used to generate the pilot flame, while intake port injected ethanol or butanol is employed as the background fuel. Compared with the conventional diesel-only combustion, dual-fuel operations not only broaden the fuel applicability, but also enhance the potential for clean combustion, in high efficiency engines. The amount of background fuel and the scheduling of pilot flame are investigated through extensive laboratory experiments.

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.

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.

Velocity prediction on hilly road can be applied to the energy-saving predictive control of intelligent vehicles. However, the existing methods do not deeply analyze the difference and diversity of road slope driving characteristics, which affects prediction performance of some prediction method. To further improve the prediction performance on road slope, and different road slope driving features are fully exploited and integrated with the common prediction method. A rolling prediction-based multi-scale fusion prediction considering road slope transition driving characteristics is proposed in this study. Amounts of driving data in hilly sections were collected by the advanced technology and equipment. The Markov chain model was used to construct the velocity and acceleration joint state transition characteristics under each road slope transition pair, which expresses the obvious driving difference characteristics when the road slope changes.

Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping.

For the purposes of on-line control, e.g., in an automatic driving system, or of closed-loop directional control simulation, an optimal preview artificial neural network (ANN) driver model based on error elimination algorithm(EEA) is built. Then the optimal preview times are discussed in high frequency range in this system. The simulation results of optimal preview ANN driver model and Error Elimination Algorithm driver model are compared under the condition of different vehicle speeds and paths, which shows that the proposed approach is efficient and reliable enough, particularly for driver-vehicle closed-loop system.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model.

Combustion and emission characteristics of diesel and biodiesel blends (soybean methyl ester) were studied in a single-cylinder Direct Injection (DI) engine at different loads and a constant speed. The results show that NOx emission and fuel consumption are increased with increasing biodiesel percentage. Reduction of smoke opacity is significant at higher loads with a higher biodiesel ratio. Compared with the baseline diesel fuel, B20 (20% biodiesel) has a slight increase of NOx emission and similar fuel consumption. Smoke emission of B20 is close to that of diesel fuel. Results of combustion analysis indicate that start of combustion (SOC) for biodiesel blends is earlier than that for diesel. Higher biodiesel percentage results in earlier SOC. Earlier SOC for biodiesel blends is due to advanced injection timing from higher density and bulk modulus and lower ignition delay from higher cetane number.

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features.

Three thermo-wires with amplifying circuits have been developed to measure the time-resolved concentration of the exhaust gas recirculated into the intake manifold by a rotary valve-based exhaust gas recirculation (EGR) system of a diesel engine. Good agreement was found between the EGR rates measured by the temperature based system and a conventional CO2 tracing system. The developed EGR measuring system was used to investigate the EGR transient response in a turbo-charged and after-cooled diesel engine with a real-time measure and control system. The EGR response under EGR valve step change and engine transient operating conditions are discussed. At first, the engine was running under a certain steady condition with zero recirculated exhaust gas, then the rotary valve opened to maximum within 0.1s to demonstrate the EGR step change behavior. EGR rate and air intake stabilized in 0.5s.

This paper examines the diesel-ethanol dual fuel combustion at medium engine loads on a single-cylinder research diesel engine with a compression ratio of 16.5:1. The effect of exhaust gas recirculation (EGR) and ethanol energy ratio was investigated for the dual fuel combustion to achieve simultaneously ultra-low NOx and soot emissions. A medium ethanol ratio of about 0.6 was found suitable to meet the requirements for mixing enhancement and ignition control, which resulted in the lowest NOx and soot emissions among the tested ethanol ratios. A double-pilot injection strategy was found competent to lower the pressure rise rate owing to the reduced fuel quantity in the close-to-TDC injection. The advancement of pilot injection timing tended to reduce the CO and THC emissions, which is deemed beneficial for high EGR operations. The reactivity mutual-modulation between the diesel pilot and the background ethanol mixture was identified.

Biodiesel is considered one of the most promising alternative fuels to petrol fuels. In this study, an attempt has been made to investigate and compare the effect of fuel injection pressure, injection timing, and exhaust gas recirculation (EGR) ratio on the particle size distributions and exhaust emissions of the diesel and biodiesel produced from waste cooking oil (WCO) used in a common rail direct injection (CRDI) diesel engine. The engine tests were conducted at two injection pressures (800 and 1600 bar), two injection timings (25 and 5 deg before top dead center (bTDC) and three EGR ratios (10%, 20% 30%) at a constant fuel injection energy per stroke and engine speed (1200 r/min). The results indicated that carbon monoxide (CO) and hydrocarbon (HC) emissions of biodiesel were slightly lower, but nitrogen oxide (NOx) emissions were slightly higher, than those of diesel fuel under most operating conditions.

Tire-road friction condition is crucial to the safety of vehicle driving. The emergence of autonomous driving makes it more important to estimate the friction limit accurately and at the lowest possible excitation. In this paper, an early detection method of tire-road friction coefficient based on pneumatic trail under cornering conditions is proposed using an intelligent tire system. The previously developed intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread. The friction estimation scheme utilizes the highly sensitive nature of the pneumatic trail to the friction coefficient even in the linear region and its approximately linear relationship with the excitation level. An indicator referred as slip degree indicating the utilization of the road friction is proposed using the information of pneumatic trail, and it is used to decide whether the excitation is sufficient to adopt the friction coefficient estimate.

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.

Driver's driving style has a great impact on lane changing behavior, especially in scenarios such as freeway on-ramps that contain a strong willingness to change lanes, both in terms of inter-vehicle interactions during lane changing and in terms of the driving styles of the two vehicles. This paper proposes a study on game-theoretic decision-making for lane-changing on highway on-ramps considering driving styles, aiming to facilitate safer and more efficient merging while adequately accounting for driving styles. Firstly, the six features proposed by the EXID dataset of lane-changing vehicles were subjected to Principal Component Analysis (PCA) and the three principal components after dimensionality reduction were extracted, and then clustered according to the principal components by the K-means algorithm. The parameters of lane-changing game payoffs are computed based on the clustering centers under several styles.

Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded.

Based on a 125cm3 single cylinder SI engine, the designated idle speed was controlled by adjusting of cycle ignition advance angle. By analyzing the effects of different idle speed and throttle open position on three way catalyst (TWC) light-off time and conversion efficiency of HC and CO emissions, combined with the corresponding total HC and CO emissions level, the optimum idle speed and throttle open position at engine's warm-up phase were found by the matching optimum. The present method for engine control strategy is helpful to optimize the warm-up phase emission levels in SI engine with LPG fuel.