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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.

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.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.

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.

Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping.

With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoon technology will be more prosperous in the future. In this article, the cooperative vehicle platoon method on the public road is represented. The method’s architecture is mainly composed of the following parts: decision-making, path planning and control command generation. The decision-making uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change requirements are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of control command generation module, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem.

A program of integrated electro-hydraulic braking system is proposed, and its structural composition and working principle are analyzed. According to the structural and mechanical characteristics of all key components, through some reasonable assumptions and simplifications, a motor, a brake master cylinder, four brake wheel cylinders, solenoid valves and an ESP (Electronic Stability Program) algorithm model is set up and simulations of typical braking conditions are carried out based on the Matlab/Simulink. Finally, after the assembly of each sub-model is complete and combining a vehicle which is set up in CarSim software environment, simulation tests and comprehensive performance analysis of the active safety stability control for a vehicle in double lane change and single lane change situations are carried out respectively. According to the dynamic characteristic curves of system, the effects of different structural and control parameters on braking performance are analyzed.

In order to study the influence of body flexibility on the truck ride comfort, a 4 DOF half vibration model of truck based on the motion synthesis between rigid body and body flexibility is established using elastic beam theory of equal section with both free ends. At the same time, a corresponding 2 DOF rigid vibration model is also built. The frequency response functions of system and response variables of two models are derived based on front wheel. The power spectral densities and the root mean square values of body acceleration, dynamic deflections and relative dynamic loads are obtained. By comparing the simulation results of rigid-elastic model and rigid model, it shows that body flexibility has a great impact on truck ride comfort and it cannot be ignored.

In order to improve the mode switching performance of parallel hybrid electric vehicles (PHEV) and make better use of the dynamics of the vehicle, this paper proposes a three-stage control method for the start-up mode of start-up, speed synchronization, and clutch slip based on the response characteristics of actual vehicle components and the complex working conditions of the actual road. In the speed synchronization phase, a coordinated control method of “engine speed active following + continuously variable transmission (CVT) speed ratio motor speed limiting” is proposed. The real vehicle test results show that the engine starting-up coordinated control method can significantly accelerate the speed synchronization and shorten the starting-up mode duration during the rapid acceleration, so that the vehicle’s power performance can be well played and the ride comfort can be effectively guaranteed.

The new control algorithm for parallel hybrid electric vehicle is presented systematically, in which engine operation points are limited within higher efficient area by the control algorithm and the state of charge (SOC) is limited in a range in order to enhance the batteries' charging and discharging efficiency. In order to determine the ideal operating point of the vehicle's engine, the control strategy uses a lookup table to determine the torque output of the engine. The off-line simulation model of parallel HEV power train is developed which includes the control system and controlled objective (such as engine, electric motor, battery pack and so on). The results show that the control algorithm can effectively limite engine and battery operation points and much more fuel economy can be achieved than that of conventional one.

Direct yaw moment control can maintain the vehicle stability in critical situation. For four-wheel independently driven (4WD) electric vehicle with in-wheel motors (IWMs), direct yaw moment control (DYC) can be easily achieved. A fairly accurate calculation of the required yaw moment can improve vehicle stability. A novel sliding mode control (SMC) technique is employed for the motion control so as to track the desired vehicle motion, which is it for different working circumstances compared to the well-used traditional DYC. Through the weighted least square algorithm, the lower controller is used to determine the torque properly allocated to each wheel according to the desired yaw moment. Several actuator constraints are considered in the control strategy. In addition, a nonlinear tire model is utilized to improve the accuracy of tire lateral force estimation. Then, simulations are carried out and the values of vehicle states are compared.

Adaptive cruise control system with lane change assistance (LCACC) is a novel advanced driver assistance system (ADAS), which enables dual-target tracking, safe lane change, and longitudinal ride comfort. To design the personalized LCACC system, one of the most important prerequisites is to identify the driver’s individualities. This paper presents a real-time driver behavior characteristics identification strategy for LCACC system. Firstly, a driver behavior data acquisition system was established based on the driver-in-the-loop simulator, and the behavior data of different types of drivers were collected under the typical test condition. Then, the driver behavior characteristics factor Ks we proposed, which combined the longitudinal and lateral control behaviors, was used to identify the driver behavior characteristics. And an individual safe inter-vehicle distances field (ISIDF) was established according to the identification results.

Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system.

To find out the main excitation sources of a bus floor's vibration, modal analysis and spectral analysis were respectively performed in the paper. First we tested the vibration modal of the bus's floor under the full-load condition, and the first ten natural frequencies and vibration modes were obtained for the source identification of the bus floor's vibration. Second the vibration characteristic of the bus floor was measured in an on-road experiment. The acceleration sensors were arranged on the bus's floor and the possible excitation sources of the bus, which includes engine mounting system, driveline system, exhaust system, and wheels. Then the on-road experiment was carefully conducted on a highway under the four kinds of test condition: in-situ acceleration, uniform velocity (90km/h, 100km/h, 110km/h, 120km/h), uniform acceleration with top gear, and stall sliding condition with neutral gear.

Starting control has become a troublesome issue in the developing field of the control system for heavy-duty trucks, due to the complexity of vehicle driving and the variability of driver's intention. The too fast clutch engagement may result in serious impact, influence on the comfort and fatigue life, and even the engine flameout, while the too slow clutch engagement may lead to long time of friction, the increased temperature, and accelerated wear of friction pair, as well as influence on the power performance and fatigue life[1]. Therefore, the key technique of starting control is clutch engagement control, for which the fuzzy PID based optimization of starting control for AMT clutch is proposed, with the pneumatic AMT clutch of heavy-duty trucks as the research object.

A general principle scheme of IEHB (Integrated Electro-Hydraulic Brake system) is proposed, and the working principle of the system is simply introduced in this paper. Considering the structure characteristics of the hydraulic control unit of the system, a kind of time-sharing control strategy is adopted to realize the purpose of independent and precise hydraulic pressure regulation of each wheel brake cylinder in various brake conditions of a vehicle. Because of the strong nonlinear and time varying characteristics of the dynamic brake pressure regulation processes of IEHB, its comprehensive brake performance is mainly affected by temperature, humidity, load change, the structure and control parameters of IEHB, and so on.

In order to improve the vehicle economy of electric vehicles, this paper first analyzes the energy-saving mechanism of electric vehicles. Taking the energy consumption of the deceleration process as a starting point, this paper deeply analyzes the energy consumption of the deceleration process under several different control modes by the test data, so as to obtain two principles that should be followed in energy-saving control strategy. Then, an intelligent deceleration energy-saving control strategy by getting the forward vehicle information is developed. The overall architecture of the control strategy consists of three parts: information processing, target calculation and torque control. The first part is mainly to obtain the forward vehicle information from the perception systems, and the user's habits information from big data, and this information is processed for the next part.

Diffuse Axonal Injury (DAI) is the most common type of traumatic brain injury, and it is associated with the linear and rotational accelerations resulting from head impacts, which often occurs in traffic related and sports accidents. To investigate the degree of influence of linear and rotational acceleration on DAI, a two-factor, two-level rat head impact experimental protocol involving linear and rotational acceleration was established using the L4(23) orthogonal table in this paper. Following the protocol, rats head was injured and diffusion tensor imaging (DTI) was performed at 24h post-injury to obtain the whole brain DAI injury, and the fractional anisotropy (FA) value of the corpus callosum was selected as the evaluation indicator. Using analysis of variance, the sum of squared deviations for the evaluation indicators was calculated to determine the degree of influence of linear acceleration and rotational acceleration on DAI. The results show that, 1.

The calibration of the electronic throttle unit and the pedal unit was made. Based on it, an electronic control system of electronic throttle was designed and installed on a 4G18 engine. Engine experiment was made especially for its transient working condition. Engine performance at transient working condition was investigated. The test results indicate that the optimum way of opening the throttle valve is to open the throttle valve to the target location at once, when 4G18 engine transit from 2000r/min to 3000r/min without load. And its optimum calibration for the electronic throttle control unit is made based on the test results. The control system, the experiment, the test results and the calibration were introduced in this paper.

A new lateral stability control method, which is based on vehicle sideslip angle and tire cornering stiffness estimation, is proposed to improve the lateral stability of the four-in-wheel-motor-driven electric vehicle (FIWMD-EV) in this paper. Through the lateral tire force information, vehicle sideslip angle can be estimated by the extended kalman filter (EKF). Using the estimated vehicle sideslip angle, tire cornering stiffness can be also estimated by forgetting factor recursive least squares (FFRLS). Furthermore, combining with the vehicle dynamics model, an adaptive control target model is proposed with the information on vehicle sideslip angle and tire cornering stiffness. The new lateral stability control system uses the direct yaw moment control (DYC) based on dynamic sliding mode is proposed. The performance and effectiveness of the proposed vehicle state estimation and lateral stability control system are verified by CarSim and Simulink cosimulation.