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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.

The China Automotive Technology and Research Center (CATARC) has completed two new wind tunnels at its test centre in Tianjin, China: an aerodynamic/aeroacoustic wind tunnel (AAWT), and a climatic wind tunnel (CWT). The AAWT incorporates design features to provide both a very low fan power requirement and a very low background noise putting it amongst the quietest in the automotive world. These features are also combined with high flow quality, a full boundary layer control system with a 5-belt rolling road, an automated traversing system, and a complete acoustic measurement system including a 3-sided microphone array. The CWT, located in the same building as the AAWT, has a flexible nozzle to deliver 250 km/h with an 8.25 m2 nozzle, and 130 km/h with a 13.2 m2 nozzle. The temperature range of the CWT is -40 °C to +60 °C with a controlled humidity range of 5% to 95%. Additional integrated systems include a variable angle solar simulator array, and a rain and snow spray system.

The rapid advancement of new energy vehicle technology has led to the widespread placement of battery packs at the bottom of vehicles. However, there is a lack of corresponding regulations and standards to guide aspects related to vehicle bottom safety. This lack of guidance obscures the relative importance of various parameters impacting the structural safety of battery packs under dynamic impact conditions. Consequently, research on battery pack bottom collisions holds practical significance and offers valuable reference material. This study proposed a method based on the first collision point to examine the impact of bottom collisions on the mechanical safety performance of battery pack bottoms. A finite element model of the battery pack was established to investigate the effects of different impact types.

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.

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.

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.

With the continuous development of the electrical vehicles (EVs), the electric power network and transportation network are interconnected by EVs which require a coordinated operation of the two networks. In view of these coupled networks, this paper proposes a charging navigation strategy for EVs based on charging power adjustment, which can not only provide the navigation path with the shortest total operational time for EVs from the origin node to the completion of charging, but also effectively reduce load fluctuations in the electric power system. In the electric power system, an innovative optimization strategy for adjusting the EV charging power distribution is proposed, which can adjust the charging power in a timely and effective manner according to the response of EV charging. The multi-objective particle swarm optimization (MOPSO) algorithm and the improved Dijkstra algorithm are used for solving the obtained the EV charging power adjustment plan and charging paths.

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.

The automotive active infrared detection system is usually applied to the night driver assistance system or the diver attention monitoring system. However, the infrared light emitted by the active infrared detection system can cause damage to retina, cornea and eye crystals. This paper has studied the photobiological safety of the infrared light source used in the automotive active infrared detection system. Although it has been already have the general requirements of photobiological safety in international standards, there is not any requirements for automotive active infrared detection system. The range of the active infrared detection system depends on the radiation intensity of the infrared light source, but too much radiation intensity will cause harm to retina, cornea and eye lens when the infrared light source is too close to eyes.

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.

Gear rattle noise is one of the important characteristics of manual and dual-clutch transmission，it is generated by the impact of unloaded meshing gear pairs in the transmission due to engine torsional vibration. Based on a front-drive manual transmission and a five dynos drivetrain NVH test bench with high-speed sine wave generator function, this paper designs an experimental program suitable for transmission rattle noise. By driving dynamometer to simulate the torque fluctuation of real engine, the main research is to study the characteristics of the transmission rattle noise under different excitation amplitudes and different excitation frequencies, and the sensitivity of rattle noise under different gears, different oil temperatures, different excitation amplitudes and excitation frequencies is analyzed. Finally, the transmission maps of rattle noise in different gears can be obtained.

Fuel cell reactors, as the core components of fuel cell vehicles, have a short life problem that has always limited the development of fuel cell vehicles. The life attenuation curve of fuel cell shows nonlinear characteristics, and there is no model that can accurately predict its effect. This paper is based on the experimental data of the vehicle fuel cell reactor, which is derived from the 600 h durability test run by a 4 kW fuel cell reactor. The relevance vector machine, as a Bayes processing method that supports vector machine, is a data-driven method based on kernel functions. The regression model is established by the relevance vector machine, and the super-parameters are found by genetic algorithm, because the kernel function strongly affects the nonlinearity of the curve, and the decay curve of fuel cell reactor performance is predicted according to four different kernel functions.

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.

Life prediction is a major focus for a commercial fuel cell stack, especially applied in fuel cell electric vehicles (FCEV). This paper proposes a data driven fuel cell lifetime prediction model using particle swarm optimized back-propagation neural network (PSO-BPNN). For the prediction model PSO-BP, PSO algorithm is used to determine the optimal hyper parameters of BP neural network. In this paper, total voltage of fuel cell stack is employed to represent the health index of fuel cell. Then the proposed prediction model is validated by the aging data from PEMFC stack in FCEV at the actual road condition. The experimental results indicate that PSO-BP model can predict the voltage degradation of PEMFC stack at actual road condition precisely and has a higher prediction accuracy than BP model.