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The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM).

Engine knock is a major challenge that limits the achievement of higher engine efficiency by increasing the compression ratio of the engine. To address this issue, using a higher octane number fuel can be a potential solution to reduce or eliminate the propensity for knock and so obtain better engine performance. Methanol, a promising alternative fuel, can be produced from conventional and non-conventional energy resources, which can help reduce pollutant emissions. Methanol has a higher octane number than typically gasolines, which makes it a viable option for reducing knock intensity. This study compared the combustion characteristics of gasoline and methanol fuels in an optical spark-ignition engine using multiple spark plugs. The experiment was carried out on a single-cylinder four-stroke optical engine. The researchers used a customized metal liner with four circumferential spark plugs to generate multiple flame kernels inside the combustion chamber.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

Ammonia (NH3) is a carbon-free fuel, which could partially or completely eliminate hydrocarbon (HC) fuel demand. Using ammonia directly as a fuel has some challenges due to its low burning speed and low flammability range, which generates unstable combustion inside the combustion chamber. This study investigated the effect of two different compression ratios (CRs) of 10.5 and 12.5 on the performance of ammonia combustion by using a conventional single spark-ignition (SI) approach. It was found that at a lower CR of 10.5, the combustion was unstable even at advanced spark timing (ST) due to poor combustion characteristics of ammonia. However, increasing the CR to 12.5 improved the engine performance significantly with lower cyclic variations. In addition, this research work also observed the effect of multiple spark ignition strategies on pure ammonia combustion and compared it with the conventional SI approach for the same operating conditions.

The tooth surface error will affect the contact pattern and transmission error of the hypoid gear, which may result in an unfavorable dynamic response. The tooth surface error can be generated by machine tool errors, such as blade wear. The most common forms of blade wear are the positive cutter radius and the positive blade angle error. In addition, in the cutting process of face-hobbed hypoid gear, the continuous indexing motion will aggravate the blade wear due to the alternating cutting force. Most previous studies on the influence of hypoid gear tool errors only focus on the contact pattern and static transmission error. However, there are very few studies about the effect of tool errors on hypoid gear dynamic responses. In this paper, a hypoid gear tooth surface, mesh, and linear dynamic model with tool errors were established. The tooth surface deviation distribution of different tool errors was analyzed.

Deep reinforcement learning (DRL) has not been widely used in the engineering field yet because RL needs to be learned through ‘trial and error’, which makes the application of this kind of algorithm in real physical environment more difficult, and it is impossible to carry out ‘trial and error’ learning on real vehicles. By analyzing the motion state of the vehicle in the car following mode, the algorithm that combined traditional longitudinal motion control with DRL improves the safety of RL in the real physical environment and the poor adaptability of the traditional longitudinal motion control algorithm. In this paper, the longitudinal motion of the unmanned vehicle is taken as the research object, and the PID algorithm is combined with the Deep Deterministic Policy Gradient (DDPG) algorithm to control the longitudinal motion of the unmanned vehicle.

Hybrid powertrain has been proven to be an effective fuel-saving technology in commercial vehicles, but many hybrid commercial vehicles still use conventional diesel engines, resulting in limited fuel savings. The main purpose of this study is to enhance the thermal efficiency of a dedicated hybrid diesel engine focusing on the characteristic operating conditions. Via fundamental thermodynamics process analysis of internal combustion engine, steel piston with high compression ratio, air system involving two-stage turbocharger(2TC) with an intercooler, and late intake valve closing(IVC) timing are proposed to improve the thermal efficiency of the engine. Experimental results show that high compression ratio and lower thermal conductivity of the combustion chamber surface lead to lower heat release rates, requiring optimization of piston profile to accelerate the mixing rate. Besides, high compression ratio also leads to higher mechanical losses.

Motor thermal management of electric vehicles (EVs) is becoming more significant due to its close relations to vehicle aerodynamic performance and power consumption, while computer aided engineering (CAE) plays an important role in its development. A 1D-3D coupled model is established to characterize transient thermal performance of the motor in an electric vehicle on a high performance computer (HPC) platform. The 1D motor thermal management model is integrated with the 1D powertrain model, and a 3D thermal model is established for the motor, while online data exchange is realized between the 1D and 3D models. The 1D model gives boundaries such as inlet coolant temperature, mass flowrate and motor heat generation to the 3D model, while the 3D model gives back boundaries such as heat transfer to coolant simultaneously. Transient simulations are performed for the 140kph(20°C) driving cycle, and the model is calibrated with experimental data.

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.

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels.

In order to ensure braking efficiency and improve the comfort of drivers and passengers, a two-stage braking grading control system was proposed. In the upper controller, the enhanced time-to-collision model under different working conditions was designed, and the braking threshold was determined considering the comfort of braking drivers and passengers, and the driver’s braking behavior was analyzed to determine the vehicle braking deceleration. The vehicle longitudinal dynamic model was built in the lower layer, the PID controller was used to reduce the model deviation. This paper improves the test standard on the basis of China-New Car Assessment Program. The results show that the remaining relative distance between the two vehicles was in the safe range. The control strategy can achieve collision avoidance of vehicle emergency braking.

In order to improve the performance of automatic emergency steering and collision avoidance of intelligent vehicle, two automatic steering control methods under ideal model following control are proposed. The two ideal reference models are the reference model with zero sideslip angle of vehicle gravity center and the reference model with no phase-lag in vehicle lateral acceleration. The control system adopts the combination of outer loop and inner loop. In the design of the outer loop controller, the optimal control is used to get the steering wheel angle needed to avoid collision. The inner loop controller uses feedforward and feedback control to get the required front and rear wheel steering angles. Taking vehicle two degrees of freedom (DOF) lateral dynamics model as the research object, the vehicle collision avoidance reference trajectory is obtained through the fifth-degree polynomial.

Automotive millimeter-wave radar is used extensively in vehicle active safety. The Radar Cross Section (RCS) is one of the main parameters used by the automotive radar system to detect and identify surrounding vehicles. The RCS describes the electromagnetic scattering properties of objects. This paper describes a method and equipment to measure the RCS. An automobile-grade radar is used to measure the RCS of typical vehicles. A representative distance between the radar and the vehicle was chosen based on the analysis of the RCS of passenger vehicles in different distances in the near field. A cost-effective rotating platform was developed to rotate the passenger vehicles for RCS measurement in different azimuth angles. The RCS generated by the rotating platform was analyzed and mitigated. The measurement system can record the synchronized azimuth angle and RCS measurement.

Aiming at the problem of poor robustness after the combination of lateral kinematics control and lateral dynamics control when an autonomous vehicle decelerates and changes lanes to overtake at a certain distance. This paper proposes a trajectory determination and tracking control method based on a PI-MPC dual algorithm controller. To describe the longitudinal deceleration that satisfies the lateral acceleration limit during a certain distance of lane change, firstly, a fifth-order polynomial and a uniform deceleration motion formula are established to express the lateral and longitudinal displacements, and a model prediction controller (MPC) is used to output the front wheel rotation angle. Through the dynamic formula and the speed proportional-integral (PI) controller to control and adjust the brake pressure.

As the impacts of global warming have become increasingly severe, Oxy-Fuel Combustion (OFC) has been widely considered as a promising solution to reduce Carbon Dioxide (CO2) for achieving net-zero emissions. In this study, a one-dimensional simulation was carried out to study the implementation of OFC technology on a practical turbocharged 4-cylinder Gasoline Direct Injection (GDI) engine with economical oxygen-fuel ratios and commercial gasoline. When the engine is converted from Conventional Air-fuel Combustion (CAC) mode to OFC mode, and the throttle opening, oxygen mass fraction, stoichiometric air-fuel ratio (lambda = 1) are kept constant, it was demonstrated that compared to CAC mode, θF gets a remarkable extension whereas θC is hardly affected. θF and θC are very sensitive to the ignition timing, and Brake Specific Fuel Consumption (BSFC) would benefit significantly from applying Maximum Brake Torque (MBT) ignition timing.

The purpose of this study is to investigate the effect of intake air hydrogenation coupled with intake air humidification (IAH) on the combustion and emission of marine diesel engines. A 3D numerical model of four-stroke turbocharged intercooled marine diesel engine was established by using commercial software AVL-Fire. The effects of hydrogen and water injected into the intake port on engine in-cylinder combustion and emission characteristics at 1350 r/min and partial load were studied. The novelty of this study is to combine different hydrogen-fuel ratios and water-fuel ratios, so as to find the optimization method that can reduce NOx and soot emissions and ensure the thermal efficiency of the engine doesn’t decrease.

In this study, a new system of assessment method was developed to evaluate the characteristics of urban buses based on remote online monitoring. Four types of buses, including China V emission standards diesel bus, lean-burn CNG bus, air-fuel equivalence ratio combustion CNG bus and gas-electric hybrid bus, were chosen as samples to analyze the emission characteristics of urban buses with different engine types in urban scenario. Based on the traffic conditions in Beijing, the actual emission characteristics of buses under newly-built driving conditions were analyzed. Moreover, the emission factor database of urban buses in Beijing was established to analyze the characteristics of excess emission. The research results are shown as follows. 1) Compared with other types of buses, NOX emission factor and emission rate of lean-burn CNG bus are much higher.

An appropriate energy management strategy can further reduce the fuel consumption of P2 hybrid electric vehicles (HEV) with simple hybrid configuration and low cost. The rule-based real-time energy management strategy dominates the energy management strategies utilized in commercial HEVs, due to its robustness and low computational loads. However, its performance is sensitive to the setting of parameters and control actions. To further improve the fuel economy of a P2 HEV, the energy management strategy of the HEV has been re-designed based on the globally optimal control theory. An optimization strategy model based on the longitudinal dynamics of the vehicle and Bellman’s dynamic programming algorithm was established in this research and an optimal power split in the dual power sources including an internal combustion engine (ICE) and an electric machine at a given driving cycle was used as a benchmark for the development of the rule-based energy management strategy.

Pumping Mean Effective Pressure (PMEP) is the main factor limiting the improvement of thermal efficiency in a spark-ignition (SI) engine under low load. One of the ways to reduce the pumping loss under low load is to use Cylinder DeActivation (CDA). The CDA aims at reducing the firing density (FD) of the SI engine under low load operation and increasing the mass of air-fuel mixture within one cycle in one cylinder to reduce the throttling effect and further reducing the PMEP. The multi-stroke cycles can also reduce the firing density of the SI engine after some certain reasonable design, which is feasible to improve the thermal efficiency of the engine under low load in theory. The research was carried out on a calibrated four-cylinder SI engine simulation platform. The thermal efficiency improvements of the 6-stroke cycle and 8-stroke cycle to the engine performance were studied compared with the traditional 4-stroke cycle under low load conditions.

Engine knock is an abnormal phenomenon, which places barriers for modern Spark-Ignition (SI) engines to achieve higher thermal efficiency and better performance. In order to trigger more controllable knock events for study while keeping the knock intensity at restricted range, various spark strategies (e.g. spark timing, spark number, spark location) are applied to investigate on their influences on knock combustion characteristics and pressure oscillations. The experiment is implemented on a modified single cylinder Compression-Ignition (CI) engine operated at SI mode with port fuel injection (PFI). A specialized liner with 4 side spark plugs and 4 pressure sensors is used to generate various flame propagation processes, which leads to different auto-ignition onsets and knock development. Based on multiple channels of pressure signals, a band-pass filter is applied to obtain the pressure oscillations with respect to different spark strategies.