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The current work presents the results of an investigation on the impact of renewable fuels on the combustion and emissions of a turbocharged compression-ignition internal combustion engine. An experimental study was undertaken and the engine settings were not modified to account for the fuel's chemical and physical properties, to analyze the performance of the fuel as a potential drop-in alternative fuel. Three fuels were tested: mineral diesel, a blend of it with waste cooking oil biodiesel and a hydrogenated diesel. The analysis of the emissions at engine exhaust highlights that hydrogenated fuel allows to reduce CO, total hydrocarbon emissions, particulate matter and NOx.

Ammonia shows promise as an alternative fuel for internal combustion engines (ICEs) in reducing CO2 emissions due to its carbon-free nature and well-established infrastructure. However, certain drawbacks, such as the high ignition energy, the narrow flammability range, and the extremely low laminar flame speed, limit its widespread application. The dual fuel (DF) mode is an appealing approach to enhance ammonia combustion. The combustion characteristics of ammonia-diesel dual fuel mode and ammonia-PODE3 dual fuel mode were experimentally studied using a full-view optical engine and the high-speed photography method. The ammonia energy ratio (ERa) was varied from 40% to 60%, and the main injection energy ratio (ERInj1) and the main injection time (SOI1) were also varied in ammonia-PODE3 mode.

Internal combustion engines, as the dominant power source in the transportation sector and the primary contributor to carbon emissions, face both significant challenges and opportunities in the context of achieving carbon neutral goal. Biofuels, such as biodiesel produced from biomass, and zero-carbon fuel ammonia, can serve as alternative fuels for achieving cleaner combustion in internal combustion engines. The dual-fuel combustion of ammonia-biodiesel not only effectively reduces carbon emissions but also exhibits promising combustion performance, offering a favorable avenue for future applications. However, challenges arise in the form of unburned ammonia (NH3) and N2O emissions. This study, based on a ammonia-biodiesel duel-fuel engine modified from a heavy-duty diesel engine, delves into the impact of adjustments in the two-stage injection strategy on the combustion and emission characteristics.

Engineering design-decisions often involve many attributes which can differ in the levels of their importance to the decision maker (DM), while also exhibiting complex statistical relationships. Learning a decision-making policy which accurately represents the DM’s actions has long been the goal of decision analysts. To circumvent elicitation and modeling issues, this process is often oversimplified in how many factors are considered and how complicated the relationships considered between them are. Without these simplifications, the classical lottery-based preference elicitation is overly expensive, and the responses degrade rapidly in quality as the number of attributes increase. In this paper, we investigate the ability of deep preference machine learning to model high-dimensional decision-making policies utilizing rankings elicited from decision makers.

Ammonia and methanol are both future fuels with carbon-neutral potential. Ammonia has a high octane number, a slow flame speed, and a narrow ignition limit, while methanol has a fast flame speed with complementary combustion characteristics but is more likely to lead to pre-ignition and knock. In this paper, the combustion and emission characteristics of ammonia-methanol solution in a high compression ratio spark ignition engine are investigated. The experimental results show that the peak in-cylinder pressure and peak heat release rate of the engine when using ammonia-methanol solution are lower and the combustion phase is retarded compared with using methanol at the same spark timing conditions. Using ammonia-methanol solution in the engine resulted in a more ideal combustion phase than that of gasoline, leading to an increase in indicated thermal efficiency of more than 0.6% and a wider range of efficient operating conditions.

Ammonia (NH3), a zero-carbon fuel, has great potential for internal combustion engine development. However, its high ignition energy, low laminar burning velocity, narrow range of flammability limits, and high latent heat of vaporization are not conducive for engine application. This paper numerically investigates the feasibility of utilizing ammonia in a heavy-duty diesel engine, specifically through low-pressure direct injection (LP-DI) of hydrogen to ignite ammonia combustion. Due to the lack of a well-corresponding mechanism for the operating conditions of ammonia-hydrogen engines, this study serves only as a trend-oriented prediction. The paper compares the engine's combustion and emission performance by optimizing four critical parameters: excess air ratio, hydrogen energy ratio, ignition timing, and hydrogen injection timing. The results reveal that excessively high hydrogen energy ratios lead to an advanced combustion phase, reducing indicated thermal efficiency.

This paper defines a control method for shift torque exchange stage and a torque distribution control method for speed regulation stage. In the torque exchange stage, the torque distribution problem of active and passive clutches considers the injection of sine curve for local correction, which can solve the fish belly problem of hydraulic response (i.e. the hydraulic response is slow at the beginning and the hydraulic response is fast at the end). In the speed regulation stage, the target speed gradient profile is determined according to different shift types. The determination of the target speed gradient profile integrates different driving modes, throttle, P2 energy and clutch temperature.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025).

To tackle the over-actuated and highly nonlinear characteristics that four-wheel-independent-steering and four-wheel-independent -driving (4WIS/4WID) vehicles exhibit when tracking aggressive trajectory, a hierarchical controller with layers of computation-intensive modules is commonly adopted. The high-level linear motion controller commands the desired state derivatives of the vehicle to meet the overall trajectory tracking objectives. Then the system dynamic is inversed by the mid-level control allocation layer and the low-level wheel control layer to map the target state derivatives to steering angle and motor torque commands. However, this type of controller is difficult to implement on the embedded hardware onboard since the nonlinear dynamic inversion is typically solved by nonlinear programming.

Hydraulic retarder is important auxiliary brake device which widely used in commercial vehicles for its economy, safety and driving comfort, however cavitation will occur and reduce the braking performance when hydraulic retarder operates at high speed. In this paper, a model of hydraulic retarder considering cavitation effect was established, and the reliability of the model was verified by comparing with the external characteristics of the product which was obtained from Voith’s official discloses data. Then the cavitation of hydraulic retarder under high-speed working condition was studied by the establishing simulation model. The simulation model can describe and analyze the internal flow field in the hydraulic retarder, and can be used as an important tool for the development and optimization of hydraulic retarder in the future. When hydraulic retarder’s rotational speed is about 1500rpm, the cavitation will be observed in the working chamber.

This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built.

The driving power system can be combined with lasers, lights, etc., and applied to automobiles to achieve various functions. Under the general trend of the development of intelligent vehicles, people have higher and higher requirements for the accuracy and power of various equipment. However, as power increases, how to ensure the stability of factors such as current is a challenging problem. Therefore, it is extremely important to study and design a high-power drive system in this paper, so as to ensure a stable output of the current. The system is composed of power supply, load, secondary power supply and control chip. The choice of power supply and load is conventional model. The secondary power supply adopts step-down circuit, with synchronous rectifier chip, which can effectively reduce energy consumption, and with temperature protection device, which can ensure the safe and reliable operation of equipment.

Octane number (ON) and octane sensitivity (S), the fuel anti-knock indices, are critical for the design of advanced jet ignition engines. In this study, ten fuels with different research octane number (RON) and varying S were formulated based on ethanol reference fuels (ERFs) to investigate the effect of S on combustion of jet ignition engine. To fully understand S effects, the combustion characteristics under EGR dilution and lean burn were further investigated. The results indicated that increasing S resulted in higher reactivity with shorter ignition delay and combustion duration. The increase of reactivity led to heavier knocking intensity. The competition between the flame speed and the reactivity of the mixture determined the auto-ignition fraction of mixture and the knocking onset crank angle as S varied. Medium S (S=3) was helpful to improve the combustion speed, reduce the auto-ignition fraction of mixture and retard the knocking onset crank angle.

High speed on-off valves (HSVs) are widely used in advanced hydraulic braking actuators, including regenerative braking systems and active safety systems, which take crucial part in improving the energy efficiency and safety performance of vehicles. As a component involving multiple physical fields, the HSV is affected by the interaction of the fields-fluid, electromagnetic, and mechanical. Since the opening of the HSV is small and the flow speed is high, cavitation and vortex are inevitably brought out so that increase the valve’s noise and instability. However, it is costly and complex to observe the flow status by visual fluid experiments. Hence, in this article a visual multi-physics system simulation model of the HSV is explored, in which the flow field model of the HSV built by computational fluid dynamic (CFD) is co-simulated with the model of hydraulic actuator established by AMESim.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Fuel quality has a significant influence on the combustion engine operation. In recent years the increasing concerns about environmental protection, energy saving, energy security and the requirements of protecting fuel injection and aftertreatment systems have been major driving forces for the Chinese fuel specification evolution. The major property changes in the evolution of Chinese national gasoline and diesel standards are introduced and the reasons behind these changes are analyzed in this paper. The gasoline fuel development from State I to State VI-B involved a decrease of sulfur, manganese, olefins, aromatics and benzene content. The diesel fuel quality improvement from State I to State VI included achieving low sulfur fuels and a cetane number (CN) increase. Provincial fuel standards, stricter than corresponding national standards, were implemented in economically developed areas in the past.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The combustion characteristics and soot emissions of three types of fuels were studied in a high pressure and temperature vessel. In order to achieve better volatility, proper cetane number and high oxygen content, the newly designed WDEP fuel was proposed and investigated. It is composed of wide distillation fuel (WD), PODE3-6 mixture (PODEn) and ethanol. For comparison, the test on WD and the mixture of PODEn-ethanol (EP) are also conducted. OH* chemiluminescence during the combustion was measured and instantaneous PLII was also applied to reveal the soot distribution. Abel transformation was adopted to calculate the total soot of axisymmetric flame. The results show that WDEP has similar ignition delays and flame lift-off lengths to those of WD at 870-920 K. But the initial ignition locations of WDEP flame in different cycles were more concentrated, particularly under the condition of low oxygen atmosphere.

In this paper, the differences between multi-hole and single-hole spray contour under the same conditions were compared by using high-speed photography. The difference between the contour area of multi-hole and that of single-hole spray was used as a parameter to describe the degree of spray collapse. Three dimensionless parameters (i.e. degree of superheat, degree of undercooling, and nozzle pressure ratio) were applied to characterize inside-nozzle thermodynamic, outside-nozzle thermodynamic and kinetic factors, respectively. In addition, the relationship between the three dimensionless parameters and the spray collapse was analyzed. A semi-empirical equation was proposed for evaluation of the degree of collapse based on dimensionless parameters of flash and non-flash boiling sprays respectively.