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A 3-D numerical analysis model of transient heat transfer among the multi-component coupling system in combustion chamber of internal combustion engine has been developed successfully in the paper. The model includes almost all solid components in combustion chamber, such as piston assembly, cylinder liner, cylinder head gasket, cylinder head, intake valves and exhaust valves, etc. With two different coupling heat transfer modes, one is the lubricant film heat conduction between two moving components, another is the contact heat conduction between two immovable solid components, and with the direct coupled-field analysis method of FEM, the heat transfer relation among the components is established. The simulation result dedicates the transient heat transfer process among the components such as moving piston assembly and cylinder liner, moving valves and cylinder head. The effect of cylinder head gasket on heat transfer among the components is also studied.

Electronic Stability Control (ESC) is an important measure to proactively guarantee vehicle safety. In this paper, the method of four-wheel hub-motor torque control is compared with the traditional single-wheel hydraulic brake control in ESC system. The control strategy adopts the hierarchical structure. In upper controller, the stability of the vehicle is identified by threshold method, the additional yaw moment control uses a way to get the moment including feedforward and feedback parts based on the linear quadratic regulator (LQR). The medium controller is tire slip rate control, in order to get the optimal target slip rate from the upper additional yaw moment, a method of quadratic programming to optimize the longitudinal force is proposed for each wheel. The inputs of tire state for the magic tire model is introduced so as to calculate the target slip rate from the target longitudinal force.

It is well-known that, compared with automatic transmissions (ATs), continuously variable transmission (CVT) shows advantages in fuel saving due to its continuous shift manner, since this feature enables the engine to operate in the efficiency-optimized region. However, as the AT gear number increases and the ratio gap narrows, this advantage of CVT is challenged. In this paper, a comparative study on fuel economy for a CVT based vehicle and a 9-speed automatic transmission (AT) based vehicle is proposed. The features of CVT and AT are analyzed and ratio control strategies for both the CVT and 9-speed AT based vehicles are designed from the view point of vehicle fuel economy, respectively. For the 9-speed AT, an optimal gear shift map is constructed. With this gear shift map, the optimal gear is selected as vehicle velocity and driving condition vary.

In this paper, a high-efficiency and low-cost lithium-ion battery pack active balance system is designed. It adopts a distributed structure and consists of three parts: auxiliary power module, one-way isolated DC/DC conversion module, and a battery group. The battery single cells in the battery pack are layered and divided into m battery groups in total, and each battery group is composed of n battery single cells. Each battery group is connected to an isolated DC/DC conversion module, and all the conversion modules are connected in parallel with the auxiliary power. Taking the SOC average value of the all-single cells in one battery group as the balancing variable, the auxiliary power is controlled to charge the battery group with the lower SOC average value, so that the difference of the SOC average value of all battery groups is within the set threshold range, so as to realize the active balance of each battery group.

Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study.

The motivation of the present work was to understand the mechanism by which alcohols produce less aromatic species in their combustion process than an equal amount of hydrocarbon with similar molecular structure does. Due to its numerous advantages over short-chain alcohols, butanol has been considered very promising in soot reduction. Excluding the influence of spray, vaporization and mixing process in engine cases, an adiabatic constant-pressure reactor model was applied to investigate the effect of butanol additives on aromatic species, which are known to be soot precursors, in fuel-rich butane flames. To keep the carbon flux constant, 5% and 10% oxygen by mass of the fuel were added to butane using butanol additive, respectively. Based on the soot reduction effects proposed in literature, effects on temperature, key radical concentrations and the carbon removal from the pathway to aromatic species were considered to identify the major mechanism of reduction in aromatic species.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

In order to solve the environmental pollution and energy crisis, Electric Vehicles (EVs) have been developed rapidly. Lithium-ion (Li-ion) battery is the key power supply equipment for EVs, and the scientific and accurate prediction of its Remaining Useful Life (RUL) has become a hot topic in the field of new energy research. The internal resistance and capacity are often used to characterize the Li-ion battery State of Health (SOH) from which RUL is obtained. However, in practical applications, it is difficult to obtain internal resistance and capacity information by using the non-intrusive measurement method. Therefore, it is necessary to extract the measurable parameters to characterize the degradation of Li-ion battery. At present, the methods of extracting health indicators based on measurable parameters have gained preliminary results, but most of them are derived from the Li-ion battery discharging data.

In automatic driving application, the velocity planner can be considered as a key factor to ensure the safety and comfort. One of the most important tasks of the velocity planner is to simulate the velocity characteristics of human drivers. In this paper, two Driver In-the-Loop (DIL) experiments are designed to explain velocity characteristics of human drivers. In the first experiment, static obstacles are placed on both sides of the straight road to shorten the cross range that vehicles can driver across. Moreover, different cross ranges are set to study the influence of the steering wheel error. In the second experiment, velocity characteristics are investigated under the condition of different road widths and curvatures in a U-turn road contour. In both tests, different drivers’ preview behavior is analyzed through the operation of throttle, braking, and steering.

Butanol, which is a renewable biofuel, has been regarded as a promising alternative fuel for internal combustion engines. When blended with diesel and applied to pilot ignited natural gas engines, butanol has the capability to achieve lower emissions without sacrifice on thermal efficiency. However, high blend ratio of butanol is limited by its longer ignition delay caused by the higher latent heat and higher octane number, which restricts the improvement of emission characteristics. In this paper, the potential of increasing butanol blend ratio by adding hot exhaust gas recirculation (EGR) is investigated. 3D CFD model based on a detailed kinetic mechanism was built and validated by experimental results of natural gas engine ignited by diesel/butanol blends. The effects of hot EGR is then revealed by the simulation results of the combustion process, heat release traces and also the emissions under different diesel/butanol blend ratios.

The high gravity center of the concrete mixer truck reduces the truck’s stability while steering. The rolling stirring tank makes the stability even worse than the regular engineering vehicle due to the dynamic variation of the centroid position. Most of the researches on the rollover stability of concrete mixer trucks focus on the rollover model establishment and dynamic simulation module. The change of concrete centroid is ignored when the safety cornering speed is calculated. This paper proposes a pre-warning method for the cornering speed of concrete mixer trucks based on centroid dynamic simulation. In the method, the mixing tank stirring model and the vehicle driving dynamic model are established on the Fluent and TruckSim simulation platforms, respectively. The theoretical speed threshold obtained by simulation is used as the evaluation index of the warning speed in the curve. Firstly, the dynamic simulation of the stirring tank model is carried out by Fluent.

In the present study, we developed a reduced chemical reaction mechanism consisted of n-heptane and toluene as surrogate fuel species for diesel engine combustion simulation. The LLNL detailed chemical kinetic mechanism for n-heptane was chosen as the base mechanism. A multi-technique reduction methodology was applied, which included directed relation graph with error propagation and sensitivity analysis (DRGEPSA), non-essential reaction elimination, reaction pathway analysis, sensitivity analysis, and reaction rate adjustment. In a similar fashion, a reduced toluene mechanism was also developed. The reduced n-heptane and toluene mechanisms were then combined to form a diesel surrogate mechanism, which consisted of 158 species and 468 reactions. Extensive validations were conducted for the present mechanism with experimental ignition delay in shock tubes and laminar flame speeds under various pressures, temperatures and equivalence ratios related to engine conditions.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

At present, the development of autonomous driving technology is still immature, and there is still a long way until fully driverless vehicles. Therefore, the state of the driver is still an important factor affecting traffic safety, and it is of great significance to detect the driver’s distracted behavior. In the task of driver distracted behavior detection, some characteristics of driver behavior in the cockpit can be further utilized to improve the detection performance. Compared with general human behaviors, driving behaviors are confined to enclosed space and are far less diverse. With this in mind, we propose a sparse spatiotemporal transformer which extracts local spatiotemporal features by segmenting the video at the low level of the model, and filters out local key spatiotemporal information associated with larger attention values based on the attention map in the middle layer, so as to enhance the high-level global semantic features.

Recovering the braking energy and reusing it can significantly improve the fuel economy of hybrid electric vehicles (HEVs).The battery ability of recovering electricity limits the improvement of the regenerative braking performance. As one way to solve this problem, the technology of brake-by-wire can be adopted in the HEVs to use the recovery dynamically. The use of high-power electrical equipment, such as electromechanical brake (EMB), is working in the form of brake-by-wire. Due to the nature of EMB, there exists an obvious coupling relationship between the energy flow and brake force distribution. In this paper, a brake force distribution controller is proposed in HEV with EMB, which can maximize braking energy recovery, compared with the conventional distribution control without EMB. Meanwhile, an energy flow strategy working with the distribution controller is designed, which is less limited to the performance of the battery.

Turbochargers can improve vehicle dynamic performance and fuel economy and are applied widely nowadays. Due to the existence of turbocharger delay effect, acceleration delay and insufficient combustion are its disadvantages. By collecting high pressure gas which generates from the inertia of the turbine in the intake passage when the vehicle slows down, the gas can be supplied for the shortage while the vehicle is accelerating, which can reduce turbocharger delay effect directly. However, turbocharger delay effect changes a little at high speed and low speed which is subjected to the air inflation and short air-release time. This paper adds a set of pressure booster device on the existing inflating-deflating device, whose thermal energy comes from the compressed air and lubricating oil, to facilitate pressure increasing in inflating-deflating device and help the chamber change sooner, which avails to relieve the delay effect.

The acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

An accurate and timely positioning of the vehicle is required at all times for autonomous driving. The global navigation satellite system (GNSS), even when integrated with costly inertial measurement units (IMUs), would often fail to provide high-accuracy positioning due to GNSS-challenged environments such as urban canyons. As a result, visual odometry is proposed as an effective complimentary approach. Although it’s widely recognized that visual odometry should be developed based on both visible and infrared images to address issues such as frequent changes in ambient lightening conditions, the mechanism of visible-infrared fusion is often poorly designed. This study proposes a Generative Adversarial Network (GAN) based model comprises a generator, which aims to produce a fused image combining infrared intensities and visible gradients, and a discriminator whose target is to force the fused image to retain as many details that exist mostly in visible images as possible.

This paper presents a method of calculating temperature field of the piston by using a wavelet neural network (WNN) to identify the unknown boundary conditions. Because of the complexity of the heat transfer and limitations of experimental conditions of heat transfer analysis of the piston in a diesel engine, boundary conditions of the piston temperature field were usually obtained empirically, and thus the result itself was uncertain. By employing the capability of resolution analysis from a wavelet neural network, the method obtains improved boundary heat transfer coefficients with a limited number of measured temperatures. Using FEA software iteratively, results show the proposed wavelet neural network analysis method improves the prediction of unknown boundary conditions and temperature distribution consistent with the experimental data with an acceptable error.

For series hybrid electric vehicles (SHEV), rule-based strategies are realistic and powerful in real-time applications. However, the previous rule-based strategy cannot strike a balance between the best fuel economy and the best battery performance while maintaining the advantages of real-time applications. In order to obtain higher efficiency and reduce battery consumption, we have developed an adaptive hybrid thermostat strategy. On the basis of maintaining the load leveling of the thermostat strategy, the threshold-changing mechanism is added to realize the adaptive adjustment of the engine starting power under different SOC conditions, so as to achieve the goal of prolonging the battery life. In addition, the more fuel-efficient emergency handling rules designed to further reduce comprehensive fuel consumption.