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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.

Due to the objectives of achieving high fuel efficiency and drivability performance, a dual-drive hybrid system with two motors has been developed. Various drive modes are presented based on engine status, requested driver torque and power, as well as C0 status in different working conditions. The transition control of drive mode change poses a unique challenge for the dual-drive hybrid system. This study discusses the control strategies for transitioning between drive modes. The first type of transition mode is divided into four distinct phases. In the second mode transition, there are three phases: the synchronization phase involving P1 torque intervention, the C0 lock-up phase involving frozen P1 torque control and adjustment of C0 clutch torque and pressure correlation, and finally, the torque exchange phase. The third type of transition includes a dedicated torque transition phase followed by a C0 disengaged phase and concluding with a speed synchronization phase.

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.

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.

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.

A novel fault tolerant brake strategy for In-wheel motor driven electric vehicles based on integral sliding mode control and optimal online allocation is proposed in this paper. The braking force distribution and redistribution, which is achieved in online control allocation segment, aim at maximizing energy efficiency of the vehicle and isolating faulty actuators simultaneously. The In-wheel motor can generate both driving torque and braking torque according to different vehicle dynamic demands. In braking procedure, In-wheel motors generate electric braking torque to achieve energy regeneration. The strategy is designed to make sure that the stability of vehicle can be guaranteed which means vehicle can follow desired trajectory even if one of the driven motor has functional failure.

Batteries are exposed to dynamic load during vehicle driving. It is significant to clarify the load input of the battery system during vehicle driving for battery pack structural design and optimization. Currently, bolt connection is mostly applied for battery pack constraint to vehicle, as well as for module assembly inside the pack. However, accurate bolt load is always difficult to obtain, while directly force measurement is expensive and time consuming in engineering. In this paper, a precise data driven model based on Elman neural network is established to identify the dynamic bolt loads of the battery pack, using tested acceleration data near bolts. The dynamic bolt force data is measured at the same time with the acceleration data during vehicle running in different driving conditions, utilizing customized bolt force sensors.

This paper has designed a real time control algorithm to use ISG motor actively compensate the torque ripple produced by the engine, to reduce torsional vibration. This paper consists of 3 parts. In the first section, this paper has introduced the research object and its modification for experiments. Then the development of control strategy is presented. The engine dynamic model is built, and real-time control with a feedforward unit and a feedback unit is derived. Encoder and cylinder pressure is used for engine torque estimator. Then the ISG motor output the counter-waveform to make the overall output smooth. In order to verify the effectiveness of the control strategy, the final section has established a test bench, where two experiments are carried out. One of the experimental conditions is to set the engine at a constant operating point, while the other is to crank the engine from 0 rpm to idle speed with ISG motor.

This paper presents an investigation of drivability issue of engine start-stop. Hybrid vehicles provide excellent benefits regarding fuel efficiency and emission. However, vibration results from constant engine start and stop events generate drivability issues, thus compromising driving comfort. This paper has designed a high speed torque sensor to capture instantaneous torque at the engine shaft. Its consequences help to find out the most suitable index of vibration severity. This paper is organized in four sections. The first section introduces the powertrain to be studied. The second section introduces development of a specially designed torque sensor. The torque sensor is installed between the engine and ISG (Integrated Starter Generator), alongside with an encoder. The torque sensor is utilized to collect the instantaneous shaft torque on occasion of engine start. In the third section, this paper has performed two experiments.

The ability of actively adjusting attitude provides a great advantage for those vehicles used in special environments such as off-road environment with extreme terrains and obstacles. It can improve vehicles’ stability and performance. This paper proposes an attitude control system for realizing the active attitude adjustment and vehicle motion control in the same time. The study is based on a vehicle with six wheel independent drive and six independent suspensions (6WIDIS), which is a kind of unmanned vehicle with six in-wheel drives and six independent hydro pneumatic suspensions. With the hydro- pneumatic suspensions, the vehicle’s attitude can be actively adjusted. This paper develops a centralized- distributed control strategy with attitude information obtained by multi-sensor fusion, which can coordinate the complex relationship among the six wheels and suspensions. The attitude control system consists of three parts.

The electromagnetic regenerative suspension has attracted much attention recently due to its potential to improve ride comfort and handling stability, at the same time recover kinetic energy which is typically dissipated in traditional shock absorbers. The key components of a ball-screw regenerative suspension system are a motor, a ball screw and a nut. For this kind of regenerative suspension, its damping character is determined by the motor's torque-speed capacity, which is different from the damping character of the traditional shock absorber. Therefore, it is necessary to establish a systematic approach for the parameter matching of ball-screw regenerative suspension, so that the damping character provided by it can ensure ride comfort and handling stability. In this paper, a 2-DOF quarter vehicle simulation model with regenerative suspension is constructed. The effects of the inertia force on ride comfort and handling stability are analyzed.

The lightweight technology takes an important role in electric vehicle(EV) energy conservation domain, as lighter vehicle means less energy consumed under same condition. In this paper, the typical energy requirement in an NEDC cycle is investigated, and the relationship between lightweight rate and energy consumption reduction effectiveness is given. The benefit of lightweight to EV come from the less battery cost because of less energy requirement. For EVs, with less battery cost, a certain lightweight rate can be obtained with less total cost. On the other hand, if lightweight rate is very high, the battery cost won't be able to cover the lightweight cost. Besides, the relationship between driving range and battery capacity is discussed in this paper. It is found that there is a limitation of EV driving range, which is determined by the battery energy density.

In order to meet increasingly stringent fuel consumption and emission regulations, more attentions are paid to improve engine efficiency. A large amount of energy-saving technologies have been applied in automotive field especially in gasoline engines. It is well known that lean burn and ultra-high compression ratio technologies are two basic and important methods to increase efficiency. In this paper, a rapid compression machine was employed to study combustion process of lean iso-octane mixture at ultra-high compression ratios (16 to 19:1). Regardless of flammability of the mixture, spark was triggered at the timing right after the end of compression, then, the flame propagation and/or auto-ignition can be recorded using high-speed photography simultaneously. The effects of equivalence ratio (φ), compression ratio (ε), dilution ratio, and effective temperature (Teff) on the combustion process was investigated.

Polyoxymethylene Dimethyl Ether (PODEn) is a promising green additive to diesel fuel, owing to the unique chemical structure (CH3O[CH2O]nCH3, n≥2) and high cetane number. Together with the general wide-distillation fuel (WDF), which has an attractive potential to reduce the cost of production of vehicle fuel, the oxygenated WDF with PODEn can help achieve a high efficiency and low emissions of soot, NOx, HC, and CO simultaneously. In this paper, the first detailed reaction mechanism (225 species, 1082 reactions) which can describe the ignition characteristics of PODE1 and PODE3 at low temperature was developed.