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Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

With the worldwide trends in mobile electrification, consumers' demand for fast charging of electric vehicles (EVs) continues to grow. However, due to the defects of the current mainstream vehicle-mounted lithium-ion batteries (LIBs), lithium plating will occur at the anode during charging at high current rates, reducing battery life and even causing serious safety problems. In this paper, a pseudo two-dimensional (P2D) model integrated with lithium plating and SEI growth reaction is established to simulate the aging behavior of the battery during the cycle aging process. After verifying the model, we set up simulation conditions to quantitatively analyze the relationship between battery operating temperature, charging rate and cycle life, as well as the causes of capacity attenuation under each operating condition.

Aiming at the problems of long lane change intention recognition, complicated lane change model, and huge amount of processing data in the current research, this paper uses principal component analysis to improve the driver’s lane change intention recognition model using traditional pattern recognition. Firstly collect 7 parameters including driver operation and vehicle running characteristics. After data standardization and PCA (principal component analysis), the top three principal components that can reflect the information content of the original data are nearly 90%. Then, a lane-change intent recognition model based on GMM-HMM was established, three lane change intents cannot be directly observed as the hidden state of the model; and three principal component quantities obtained through linear changes are used as observational measurements.

A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

The poor low-temperature behavior of Li-ion batteries has limited its application in the field of electric vehicles and hybrid electric vehicles. Many previous studies concentrate on developing new type of electrolyte to solve this problem. However, according to recent research, the key limitation at low temperature is the low diffusivity of lithium ion in the anode electrodes. Hence, it is potential to study anode materials to improve low-temperature behavior of LIBs. ZnFe2O4 with higher theoretical capacity is low toxicity and abundance, contributing to its commercial application. Different ZnFe2O4 crystalline shapes have different particle sizes. Among them, the cubic ZnFe2O4 with smaller particle size will increase its own electronic and ionic conductance at lower temperature. In this regard, we evaluated low-temperature performance of LIBs with ZnFe2O4 cubes as anode materials at -25°C.

At the collision moment, a driver’s lower extremity will be in different foot position, which leads to the different posture of the lower extremity with various muscle activations. These will affect the driver’s injury during collision, so it is necessary to investigate further. A simulated collision scene was constructed, and 20 participants (10 male and 10 female) were recruited for the test in a driving simulator. The braking posture and muscle activation of eight major muscles of driver’s lower extremity (both legs) were measured. The muscle activations in different postures were then analyzed. At the collision moment, the right leg was possible to be on the brake (male, 40%; female, 45%), in the air (male, 27.5%; female, 37.5%) or even on the accelerator (male, 25%; female, 12.5%). The left leg was on the floor all along.

This paper describes a novel recognition and classification method of vehicle targets in urban road based on a vehicle-mounted Velodyne HDL64E light detection and ranging (LIDAR) system. The autonomous vehicle will choose different driving strategy according to the surrounding traffic environments to guarantee that the driving is safe, stable and efficient. It is helpful for controller to provide the efficient stagey to know the exact type of vehicle around. So this method concentrates on reorganization and classification the type of vehicle targets so that the controller can provide a safe and efficient driving strategy for autonomous ground vehicles. The approach is targeted at high-speed ground vehicle, so real-time performance of the method plays a critical role. In order to improve the real-time performance, some methods of data preprocessing should be taken to simplify the large-size long-range 3D point clouds.

According to the process that a new driver becomes a low skill level driver and finally a skilled driver from learning how to drive, especially in light of the understanding on the vehicle lateral dynamics that will change from linear characteristic under low speed to strong nonlinear character under high speed, a novel driver model is established. At low speed linear range, off-line optimization based on genetic tuning is introduced into the model to get the optimal control parameters which is viewed as a basic understanding of the vehicle dynamic characteristics of a low skill level driver. On basis of the previous established model, neural network adaptive mechanism is introduced to the driver model which enables the driver to adjust the control online even at high speed non-linear area, reflecting a deeper understanding of the vehicle dynamic model. At last, simulation has been taken in order to verify the correctness and accuracy of the model.

The behavior of driver course decision making is analyzed with the theory of system fuzzy decision making, and some factors that influence this behavior are studied also. Based on these, a fuzzy decision making model of driver dynamically determining vehicle preview course is given. This model can simulate the driver's control behavior of deciding the vehicle preview course in the process of driver handling the vehicle, based on the feasible region of front road. Taking advantage of fuzzy decision making theory's character, the model can describe many decision criteria such as driving safety, driving handiness and driving legality. The simulation results show that the model can be directly applied into the simulation of driver-vehicle-road closed-loop system and the research of intelligent vehicle.

The research of driver behavior characteristics has been a focus of vehicle handling and stability performance. With the driver preview effort, many different driver preview models of direction control have been proposed and the simulations of driver-vehicle-road closed-loop system made. But in the simulation, most of the conventional models have the same precondition that the road was simply described as a pre-given preview course. How to simulate the driver dynamically deciding vehicle preview course based on the real road circumstance is the key to the further research of the driver model. In this paper, a new driver direction control model is established, which is called the Optimal Preview Lateral Acceleration (OPLA) Model and divided into three sub-models: driver’s information identification model, driver’s fuzzy decision model of vehicle preview course and driver’s performance first-order correction model.