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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.

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.

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.

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.

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.

A boost system with an auxiliary gas turbine used to recover diesel engine power at plateau conditions is proposed. System matching calculation, preliminary design, and performance simulation of the compressor with double parameter output are presented, as well as the preliminary design, flow simulation, and combustion process of the combustion chamber. Results show that the new system has better recovery performance and higher fuel economy potential than the simple charging scheme. For future research work, possible improvements and development direction are recommended.

There are a few different ways in which biofuels can be sourced, with the most popular coming from agricultural sources. An alternative approach is to utilize biowaste. An estimated 20 million dry tons of volatile organic compounds, or biowaste, is annually deposited in US municipal wastewaters. Most of this biowaste energy content is not recovered and, as a result, the biowaste could be a massive potential source of renewable energy. Biocrude diesel is converted from wet biowaste via hydrothermal liquefaction (HTL). Three types of feedstocks (algae, swine manure, and food processing waste) were converted into biocrude oil via HTL. From the previous experiments done in an AVL 5402 single-cylinder diesel engine, it was observed that the presence of 20% of HTL in the blend performed similarly during combustion to pure diesel. By studying these mixtures in a constant volume chamber, these observations could be compared to the results in the diesel engine.

This present paper described an experimental study on the combustion and emission characteristics of a diesel engine at idle at different altitudes. Five altitudes ranging from 550m to up to 4500m were investigated. Combustion parameters including in-cylinder pressure and temperature, heat release, fuel mass burning and so forth, together with emission factors including CO, HC, NOx and PM were tested and analyzed. The result of on-board measurement manifested that in-cylinder pressure descended consistently with the rising of altitude, while both the maximum in-cylinder temperature and exhaust temperature ascended with the altitude. It was found that ignition delay was lengthened at higher altitude, but the combustion duration became shorter. The crank angle towards 90% fuel burnt has hardly changed with the variation of altitude. As for heat release, the difference of slopes observed at different altitudes was quite slight.

Bio-butanol has been considered as a promising alternative fuel for internal combustion engines due to its advantageous physicochemical properties. However, the further development of bio-butanol is inhibited by its high recovery cost and low production efficiency. Hence, the goal of this study is to evaluate two upstream products from different fermentation processes of bio-butanol, namely acetone-butanol-ethanol (ABE) and isopropanol-butanol-ethanol (IBE), as alternative fuels for diesel. The experimental comparison is conducted on a single-cylinder and common-rail diesel engine under various main injection timings (MIT) and equivalent engine load (EEL) conditions. The experimental results show that ABE and IBE significantly affect the combustion phasing. The start of combustion (SOC) is retarded when ABE and IBE are mixed with diesel. Furthermore, the ABE/IBE-diesel blends are more sensitive to the changes in MIT compared with that of pure diesel.

Study and modeling of diesel combustion during transient operations is an important scientific objective. This is partially due to the fact that emissions under transient operations have aroused increasing attention by control groups during recent decades. The objective of this paper is to develop a combustion model to predict the peculiarities of transient combustion for developing and testing control strategies. To by-pass the complicated principles of transient combustion, the Neural Networks are applied to link the coefficients in an empirical combustion model with engine operating parameters. Finally, the Neural Networks combustion model would not only reflect the influence of turbocharge lag on combustion process during transient event, which cannot be predicted by its interpolation alternative, but also shown great potential for analyzing combustion characteristics during load increase transient event or other transient operations.

Based on MATLAB/Simulink, the ECU application software components library for diesel engine has been designed in this paper. The hierarchic and modularized components library is an open research platform for the model-based control software development. Using the components technology, the requirements of the diesel engine ECU application software have been analyzed, upon which the detailed components partition and the components library design have been accomplished. Besides, based on this components library, a control prototype for the diesel engine has been established quickly and verified through the Hardware-in-the-Loop test. The ECU software design and test process based on the components library show a good flexibility of the library, and it can improve the configurability and reusability of the software and increase the efficiency of the control software development.

In this paper, a new-type balanced opposed-piston folded-cranktrain (OPFC) two-stroke diesel engine is developed by Beijing Institute of Technology. OPFC has some potential advantages such as simple structure, good balance, compact, high power density and thermal efficiency. The structural feature of OPFC engine leads to the performance is different with the conventional engine. In order to study and verify the characteristics of this kind of engine, the folded-crank train dynamics, cylinders scavenging process and combustion process are investigated. The influence of parameters on the engine performance is investigated, includes the fuel injection timing, intake/exhaust port timing. In addition, the nozzle diameter is investigated as a main factor to affect the mixture and combustion process in the cylinder.

A multi-task control template has been established for diesel engine control system in this paper. By analyzing the function requirements and control strategies of electronic diesel engine control system, the data flow diagram has been obtained. Based on the mechanisms and characteristics of the RTOS as well as the design methods of real-time system software, modules in data flow graph has been divided further. So that the multi-tasks of the control system have been drawn. Then each task's properties have been designed. Afterwards, every task and the embedded RTOS have been transplanted into Freescale MPC5554 microcontroller. After that the multi-tasks have been tested and optimized in a Hardware-in-the-Loop Simulation (HILS) system of dSPACE AUTOBOX. The results show good reusability and real-time performance and it can increase the efficiency of the diesel engine control software development.

The ever-growing number of interacting electronic vehicle control systems requires new control algorithms to manage the increasing system complexity. As a result, torque-based control architecture has been popular for its easy extension as the torque demand variable is the only interface between the engine control algorithms and other vehicle control systems. Under the torque-based control architecture, the engine and AT coordinated control for upshift process is investigated. Based on the dynamics analysis, quantitative relationship between the turbine torque of HTC and output shaft torque of AT has been obtained. Then the coordinated control strategy has been developed to smooth the torque trajectory of AT output shaft. The designed control strategy is tested on a powertrain simulation model in MATLAB/Simulink and a test bench. Through simulation, the shift time range in which the engine coordinated control strategy is effective is acquired.

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.