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The thermal performance of an engine coolant system is efficient when the engine head temperature is maintained within its optimum working range. For this, it is desired that air should not be entrapped in the coolant system which can lead to localized hot spots at critical locations. However, it is difficult to eliminate the trapped air pockets completely. So, the target is to minimize the entrapped air as much as possible during the coolant filling and deaeration processes, especially in major components such as the radiator, engine head, pump etc. The filling processes and duration are typically optimized in an engine test stand along with design changes for augmenting the coolant filling efficiency. However, it is expensive and time consuming to identify air entrapped locations in tests, decide on the filling strategy and make the design changes in the piping accordingly.

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.

This paper introduces the characteristics of the 4 wheel independent driving/4 wheel independent steering (4WID/4WIS) electric vehicle (EV). Models of Subsystems and the vehicle are constructed based on Matlab/simulink. The vehicle model allows the inputs of different drive torques and steer angles of four wheels. The dynamic characteristics of drive motors and steer motors are considered, and also it can reflect the vehicle longitudinal dynamics change due to the increase of the mass and inertia of the four wheels. Besides, drive mode selection function that is unique to this type vehicle is involved. Simulations and analyses of crab, oblique driving and zero radius turning which are the special conditions of 4WID/4WIS EV are conducted. The results show that the model can reflect the dynamic response characteristics. The model can be used to the simulation analyses of handling, stability, energy saving and control strategies verification of 4WID/4WIS EVs.

A braking force distribution strategy in integrated braking system composed of the main braking system and the auxiliary braking system based on braking pad wear control and hitch force control under non-emergency braking condition is proposed based on the Electronically Controlled Braking System (EBS) to reduce the difference in braking pad wear between different axles and to decrease hitch force between tractors and trailers. The proposed strategy distributes the braking force based on the desired braking intensity, the degree of the braking pad wear and the limits of certain braking regulations to solve the coupling problems between braking safety, economical efficiency of braking and the comfort of drivers. Computer co-simulations of the proposed strategy are performed.

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.

Inerters have become a hot topic in recent years, especially in vehicle, train, and building suspension systems. The performance of a passive inerter and a semi-active inerter was analyzed and compared with each other and it showed that the semi-active inerter has much better performance than the passive inerter, especially with the Hybrid control method. Eight different layouts of suspensions were analyzed with a quarter car model in this paper. The adaptation of dimensionless parameters was considered for a semi-active suspension and the semi-active inerters. The performance of the semi-active inerter suspensions with different layouts was compared with a semi-active suspension with a conventional parallel spring-damper arrangement. It shows a semi-active suspension, with more simple configuration and lower cost, has similar or better compromise between ride and handling than a semi-active inerter with the Hybrid control.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

The driving behaviors of young drivers under the influence of anger are analyzed by driving simulator in this paper. A total of 12 subjects are enrolled during the experiment. Standardized videos are utilized to induce the driver's anger emotion. And the driver's electrocardiogram (ECG) signal is collected synchronously and compared before and after emotional trigger, which prove the validity of emotional trigger. Based on the result, the driver's driving performance under the straight road and the curve under normal state and angry state are compared and analyzed. The results of independent sample t-test show that there are significant differences in the running time of straight sections and the standard deviation of steering wheel angle in curves between normal and angry states. In conclusion, the longitudinal and lateral operation of drivers is unstable in angry state and the driver will be more destructive to the regular driving behavior.

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

Diesel engines are far more efficient than gasoline engines of comparable size, and emit less greenhouse gases that have been implicated in global warming. In 2000, the US EPA proposed very stringent emissions standards to be introduced in 2007 along with low sulfur (< 15 ppm) diesel fuel. The California Air Resource Board (CARB) has also established the principle that future diesel fueled vehicles should meet the same low emissions standards as gasoline fueled vehicles and the EPA followed suit with its Tier II emissions regulation. Achieving such low emissions cannot be done through engine development and fuel reformulation alone, and requires application of NOx and particulate matter (PM) aftertreatment control devices. There is a widespread consensus that NOx adsorbers and particulate filter are required in order for diesel engines to meet the 2007 emissions regulations for NOx and PM. In this paper, the key exhaust characteristics from an advanced diesel engine are reviewed.

Research of Adaptive Cruise Control (ACC) is an important issue of intelligent vehicle (IV). As we all known, a real and experienced driver can control vehicle's speed very well under every traffic environment of ACC working. So a direct and feasible way for establishing ACC controller is to build a human-like longitudinal control algorithm with the simulation of driver behavior of speed control. In this paper, a novel fuzzy self-tuning control algorithm of ACC is established and this controller's parameters can be tuned on-line based on the evaluation indexes that can describe how the driver consider the quality of dynamical characteristic of vehicle longitudinal dynamics. With the advantage of the controller's parameter on-line self-tuning, the computational workload from matching design of ACC controller is also efficiently reduced.

Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, we discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model.

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

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.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

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.

Taking a good longitudinal braking performance on flat and level road of tractor and semi-trailer combination as a target, in order to achieve an ideal braking force distribution among axles, while the vehicle deceleration is just depend on the driver's intention, not affected by the variation of semi-trailer mass, the paper proposes a model based vehicle mass identification and braking force distribution strategy. The strategy identifies the driver's braking intention via braking pedal, estimates semi-trailer's mass during the building process of braking pressure in brake chamber, distributes braking force among axles by using the estimated mass. And a double closed-loop regulation of the vehicle deceleration and utilization adhesion coefficient of each axle is presented, in order to eliminate the bad effect of mass estimation error, and enhance the robustness of the whole algorithm. A simulation is conducted by utilizing MATLAB/Simulink and TruckSim.