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The acquisition of more authentic cylinder pressure data is the basis of engine combustion analysis. Due to the multiple advantages, quartz piezoelectric pressure transducers are generally applied to the measurement of the cylinder pressure. However, these transducers can only produce dynamic cylinder pressure data which may be significantly different from the actual values. Thus, the cylinder pressure data need to be corrected through a certain method, while different cylinder pressure correction methods will cause result divergences of the combustion analysis. This paper aims to acquire a proper cylinder pressure correction method by carrying out theoretical analysis based on the polytropic process in the compression stroke as well as the experimental research of the cylinder pressure of a turbocharged eight-cylinder diesel engine.

Hydrogen is a promising energy carrier because it is characterized by a fast combustion velocity, a wide range of sources, and clean combustion products. A hydrogen internal combustion engine (H2ICE) with a turbocharger has been used to solve the contradiction of power density and control NOx. However, the selection of a H2ICE compressor with a turbocharger is very different from traditional engines because of gas fuel. Hydrogen as a gas fuel has the same volume as its cylinder and thus increases pressure and reduces the mass flow rate of air in cylinder for a port fuel injection-H2ICE (PFI-H2ICE). In this study, a general method involving a H2ICE with a turbocharger is proposed by considering the effect of hydrogen on cylinders. Using this method, we can calculate the turbocharged pressure ratio and mass flow rate of air based on the target power and general parameters. This method also provides a series of intake temperatures of air before calculation to improve accuracy.

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.

With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. To seek for an optimized volumetric ratio for ABE-diesel blends, the previous work in our team has experimentally investigated and analyzed the combustion features of ABE-diesel blends with different volumetric ratio (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %) in a constant volume chamber. It was found that an increased amount of acetone would lead to a significant advancement of combustion phasing whereas butanol would compensate the advancing effect. Both spray dynamic and chemistry reaction dynamic are of great importance in explaining the unique combustion characteristic of ABE-diesel blend. In this study, a semi-detailed chemical mechanism is constructed and used to model ABE-diesel spray combustion in a constant volume chamber.

In order to meet the need of high pressure common rail diesel engine, calibration for injection quantity and basic MAP of electronic-controlled injector are made. Combining with testing data, influencing factors for consistency and identity of injecting fuel in electronic-controlled injector are analyzed, in the condition of small quantity, controlled-pressure undulation quantity and injecting pulse revising are presented to achieve the respective demand. Primary basic map for common rail pressure and injecting fuel are fixed with alterable step method, and calibration of fuel quantity MAP is made on bench test. Finally test of electronic-controlled injector equipped in diesel engine is finished, testing result showed that calibration process and method are reasonable.

NOx are the only harmful emissions of hydrogen internal combustion engine. EGR is one of the effective methods to reduce NOx. The traditional EGR is not suitable for hydrogen internal combustion engine. Therefore, the study of influence of hot EGR on hydrogen internal combustion engine is important. A 2.0L hydrogen internal combustion engine with hot EGR system model is employed to optimize the diameter and position of hot EGR based on a simulation analysis. The result shows that both of the combustion temperature and NOx increase as EGR increases due to the rise of intake temperature for low load condition, for heavy load, with the increase of EGR rate, NOx emissions decreases slightly before the mixture equivalence ratio comes to 1and then dropped significantly after the mixture equivalence ratio greater than 1. Unburned hydrogen in TWC has the effect of reducing NOx after catalysts decrease largely.

This paper presents a study of LPG lean burn in a motorcycle SI engine. The lean burn limits are compared by several ways. The relations of lean burn limit with the parameters, such as engine speed, compression ratio and advanced spark ignition etc. are tested. The experimental results show that larger throttle opening, lower engine speed, earlier spark ignition timing, larger electrode gap and higher compression ratio will extend the lean burn limit of LPG. The emission of a LPG engine, especially on NOx emission, can be significantly reduced by means of the lean burn technology.

Nowadays, the world is facing severe energy crisis and environment problems. Development of hydrogen fuel vehicles is one of the best ways to solve these problems. Due to the difficulties of infrastructures, such as the hydrogen transport and storage, hydrogen fuel vehicles have not been widely used yet. As a result, Hydrogen-gasoline dual-fuel vehicle is a solution as a compromise. In this paper, three way catalytic converter (TWC) was used to reduce emissions of hydrogen-gasoline dual-fuel vehicles. On wide open throttle and load characteristics, the conversion efficiency of TWC in gasoline engine was measured. Then the TWC was connected to a hydrogen internal combustion engine. After switching the hydrogen and gasoline working mode, emission data was measured. Experiment results show that the efficiency of a traditional TWC can be maintained above 85%., while it works in a hydrogen-gasoline dual-fuel alternative working mode.

The low cycle fatigue experiment is extensively used to test the reliability and durability of turbocharger. Low cycle fatigue test is mainly the switching between high and low speed. As the result of the experiment, the fatigue life is shorter as the difference between high and low speed becomes greater. In the traditional low cycle fatigue test, a large air compressor is needed to drive the turbocharger under different operating conditions, which consume large amounts of electric power. This paper presents a new experiment device which has double chambers and double turbochargers. This device can be self-circulating, without the large air compressor, to realize high and low speed switching on the premise of not exceeding the limitation of turbine entry temperature. First, a detailed model is established in GT-Power and self-circulation test data has been used to validate the model.

When the EFI system is used in a specific engine, lots of experiments are needed to optimize the control data (MAP). This work is time and financial consuming. This paper aims to develop a computer-based simulation and test system, which can produce the initial control MAP with good accuracy, and calibrate the ECU on-line. So the experiments are reduced and calibration is accelerated. In order to improve the accuracy of the initial control data, the mathematical models are built not only based on theoretical equations, but also on the control data of typical operation points, which is obtained by the on- line calibration of specific engines. This system can also perform some special calibrations, like "constant pulse width" and "square wave modulation."

Active control of low-frequency engine order noise helps to improve the passenger’s sense of hearing, so it has become one of the hot topics in the automotive field. Depth improvement of active noise control (ANC) performance from the perspective of novel algorithms has attracted the attention of researchers. The conventional notch dual-channel filtered-x least mean square (NDFxLMS) algorithm shows acceptable noise reduction for the elimination of engine order noise. To further enhance the steady-state ANC effect, this paper proposed two new notch algorithms: the notch dual-channel filtered-x recursive least square (NDFxRLS) algorithm and the notch dual-channel affine projection (NDAP) algorithm. Vehicle simulation tests show that both the proposed algorithms, especially the NDFxRLS algorithm, have a satisfying performance for the cancellation of interior noise from the engine.

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.

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.

Turbocharged and intercooled DI engines, fueled with different blends of biodiesel and diesel fuel, were chosen to conduct performance and emission tests on dynamometers. The properties of the test fuels were tested. The cylinder pressure and fuel injection pressure signals were recorded and combustion analysis was conducted. The engine exhaust emissions were measured. The results of the study indicated that HC, CO, PM and smoke emissions improvement was obtained. But there was an increase in fuel consumption and NOx emission, and a slight drop in power with the blends. The combustion analysis showed that biodiesel had a shorter ignition delay and a lower premixed combustion amount, but had an early start of injection caused by the fuel properties. The relationship between combustion and emissions was discussed.

Dual-fuel combustion combining a premixed charge of compressed natural gas (CNG) and a pilot injection of diesel fuel offer the potential to reduce diesel fuel consumption and drastically reduce soot emissions. In this study, dual-fuel combustion using methane ignited with a pilot injection of No. 2 diesel fuel, was studied in a single cylinder diesel engine with optical access. Experiments were performed at a CNG substitution rate of 70% CNG (based on energy) over a wide range of equivalence ratios of the premixed charge, as well as different diesel injection strategies (single and double injection). A color high-speed camera was used in order to identify and distinguish between lean-premixed methane combustion and diffusion combustion in dual-fuel combustion. The effect of multiple diesel injections is also investigated optically as a means to enhance flame propagation towards the center of the combustion chamber.

The combustion characteristics of gasoline-diesel dual-fuel in an electronic-controlled high pressure common rail optical engine were investigated under different diesel injection timings and gasoline/diesel ratios by a high-speed photography method. The experimental results show that the dual-fuel combustion process is influenced by diesel combustion and gasoline homogenous combustion, respectively, with bright yellow flames and blue flames observed in the combustion chamber. At a gasoline/diesel ratio of 0.91, the injection timing affects the ignition timing and combustion modes significantly. When the diesel injection timing is before −25° after top dead center (ATDC), advancing the injection timing tends to prolong the ignition delay and the gasoline-diesel dual-fuel combustion is similar to the pre-mixed charge compression ignition (PCCI) combustion with a rapid single-stage heat release.