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The Electronic Mechanical Braking (EMB) system, which offers advantages such as no liquid medium and complete decoupling, can meet the high-quality active braking and high-intensity regenerative braking demands proposed by intelligent vehicles and is considered one of the ideal platforms for future chassis. However, traditional control strategies with fixed clamping force tracking parameters struggle to maintain high-quality braking performance of EMB under variable braking requests, and the nonlinear friction between mechanical components also affects the accuracy of clamping force control. Therefore, this paper presents an adaptive clamping force control strategy for the EMB system, taking into account the resistance of nonlinear friction. First, an EMB model is established as the simulation and control object, which includes the motor model, transmission model, torque balance model, stiffness model, and friction model.

Ambient temperature is a very sensitive use condition for electric vehicles (EVs), so it is imperative to ensure the maintenance of suitable temperature. This is particularly important in regions characterized by prolonged exposure to unfavorable temperature conditions. In such cases, it becomes necessary to implement insulation measures within parking facilities and allocate energy resources to sustain a desired temperature level. Solar energy is a renewable and environmentally friendly source of energy that is widely available. However, the effectiveness of utilizing solar energy is influenced by various factors, such as the time of day and weather conditions. The use of phase change material (PCM) in a latent heat energy storage (LHES) system has gained significant attention in this field. In contrast to single-phase energy storage materials, PCM offer a more effective heat storage capacity.

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.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

It is generally considered that the material properties of foam are the most important factors in vehicle seat, which affect the human-seat interface pressure. Therefore, only the role of foam is usually considered when the finite element method is used to simulate the human-seat interface pressure. In this paper, the mechanical properties and the modeling method of commonly used seat cover material were studied. The models of the seat with and without cover were established respectively according to the real-vehicle seat geometric data, and the human-seat interface pressure was simulated after the seat and human model consisting of bones, soft tissue and skin were assembled. The simulation result was compared with the actual measurement results from test, which verified the accuracy of the simulation and the role of seat cover in the human-seat interface pressure simulation.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

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.

Autonomous driving technology, as the product of the fifth stage of the information technology revolution, is of great significance for improving urban traffic and environmentally friendly sustainable development. Autonomous driving can be divided into three main modules. The input of the decision module is the perception information from the perception module and the output of the control strategy to the control module. The deep reinforcement learning method proposes an end-to-end decision-making system design scheme. This paper adopts the Deep Deterministic Policy Gradient Algorithm (DDPG) that incorporates the Priority Experience Playback (PER) method. The framework of the algorithm is based on the actor-critic network structure model. The model takes the continuously acquired perception information as input and the continuous control of the vehicle as output.

With the increasingly complex traffic environment, the vehicle AEB system needs to go through a large number of testing processes, in order to drive more safely on the road. For speeding up the development process of AEB and solve the problems of long cycle, high cost and low efficiency in AEB testing, in this paper, a millimeter wave radar turntable is built, and a high-precision detection algorithm of turntable encoder is designed, at the same time, a test method of vehicle AEB based on the detection data of radar turntable encoder is designed. The verification results show that methods described in this paper can be used to develop the vehicle AEB test algorithm efficiently.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

With the development of intelligent and electric vehicles, higher requirements are put forward for the active braking and regenerative braking ability of the braking system. The traditional braking system equipped with vacuum booster has difficulty meeting the demand, therefore it has gradually been replaced by the integrated braking system. In this paper, a novel Integrated Braking System (IBS) is presented, which mainly contains a pedal feel simulator, a permanent magnet synchronous motor (PMSM), a series of transmission mechanisms, and the hydraulic control unit. As an integrative system of mechanics-electronics-hydraulics, the IBS has complex nonlinear characteristics, which challenge the accurate pressure control. Furthermore, it is a completely decoupled braking system, the pedal force doesn’t participate in pressure-building, so it is necessary to precisely identify driver’s braking intention.

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

The purpose of this study is to evaluate the impact of two-stage pilot injection parameters on the combustion and emissions of pilot diesel compression ignition natural gas (CING) engine at low load. Experiments were performed using a diesel/natural gas dual-fuel engine, which was modified from a six-cylinder diesel engine. The effect of injection timing and injection pressure of two-stage pilot diesel were analyzed in order to reduce both the fuel consumption and total hydrocarbon (HC) and carbon monoxide (CO) emissions under low load conditions. The results indicate that, because injection timing can determine the degree of pilot diesel stratification, in-cylinder thermodynamic state, and the available mixing time prior to the combustion, the combustion process can be controlled and optimized through adjusting injection timing.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

The Electro-Mechanical Brake Booster system (EMBB) is a kind of novel braking booster system, which integrates active braking, regenerative braking, and other functions. It usually composes of a servo motor and the transmission mechanism. EMBB can greatly meet the development needs of vehicle intelligentization and electrification. During active braking, EMBB is required to respond quickly to the braking request and track the target pressure accurately. However, due to the highly nonlinearity of the hydraulic system and EMBB, traditional control algorithms especially for PID algorithm do not work well for pressure control. And a large amount of calibration work is required when applying PID algorithms to pressure control in engineering.

In order to solve the coupling problems between braking safety, economical efficiency of braking and the comfort of drivers, a braking control strategy based on Electronically Controlled Braking System (EBS) and intelligent network technology under non-emergency braking conditions is proposed. The controller utilizes the intelligent network technology’s characteristics of the workshop communication to obtain the driving environment information of the current vehicle firstly, and then calculate the optimal braking deceleration of the vehicle based on optimal control method. The strategy will distribute the braking force according to the ideal braking force distribution condition based on the EBS according to the braking deceleration; the braking force will be converted to braking pressure according to brake characteristics. Computer co-simulations of the proposed strategy are performed, the strategy is verified under different initial speeds.

A vehicle’s NVH performance has a significant impact on the user experience of the driver and passengers. About one-third of the vehicle complaints are related to NVH performance. As the core component of the vehicle, the drivetrain’s NVH characteristics have a significant impact on vehicle comfort. How to reliably and stably reproduce the specific condition of the whole vehicle through the test method, and obtain the highly consistent objective data for analyzing and improving the NVH characteristics of the drivetrain is of great significance in engineering. For this purpose, China Automotive Technology Research Center Co., Ltd. (CATARC) designed and built a new type drivetrain NVH test facility, which consists of five dynamometers, and can carry horizontal/vertical, front/rear drive or four-wheel drive structures including powertrain, transmission, and rear axle, or even a whole vehicle.

The underbody diffusers are used widely in race cars to improve the flow field structure at the bottom of the car and provide enough downforce. In recent years, passenger cars have begun to use bottom diffuser to improve aerodynamic characteristics, so as to reduce drag and increase downforce. In this paper, the aerodynamic characteristics of the bus with different underbody diffuser angles and channel numbers are studied by numerical simulation analysis. Firstly, the aerodynamics of the bus under different diffuser inlet and outlet angles are studied, and then an optimal inlet and outlet angle is determined based on the simulation results. Then, using this angle as a constant, the 2, 3, and 4 channel numbers were chosen as the diffuser channel variables to study the influence of the multiple-channel diffusers on the aerodynamic drag of the vehicle.

The Electronic Stability Program (ESP), as a key actuator of traditional automobile braking system, plays an important role in the development of intelligent vehicles by accurately controlling the pressure of wheels. However, the ESP is a highly nonlinear controlled object due to the changing of the working temperature, humidity, and hydraulic load. In this paper, an accurate pressure control strategy of single wheel during active braking of ESP is proposed, which doesn’t rely on the specific parameters of the hydraulic system and ESP. First, the structure and working principle of ESP have been introduced. Then, we discuss the possibility of Pulse Width Modulation (PWM) control based on the mathematical model of the high-speed switching valve. Subsequently, the pressure building characteristics of the inlet and outlet valves are analyzed by the hardware in the Loop (HiL) experimental platform.