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The adhesion control is the basic technology of active safety for the four-wheel driven EV. In this paper, a novel adhesion control method based on fuzzy logic control is proposed. The control system can maximize the adhesion force without road condition information and vehicle speed signal. Also, the regulation torque to prevent wheel slip is smooth and the vehicle driving comfort is greatly improved. For implementation, only the rotating speed of the driving wheel and the motor driving torque signals are needed, while the derived information of the wheel acceleration and the skid status are used. The simulation and road test results have shown that the adhesion control method is effective for preventing slip and lock on the slippery road condition.

With increasing pressure from environment problem for reduction in CO2 emissions and stricter fuel targets from road vehicles, new transmission technologies are gaining more attention in different main market. To get suitable road load data for transmission durability development is becoming increasingly important and can shorten the development time of new transmission. This paper presents the procedure and methods of road load data development for durability design of transmission product and optimization based on the real road data measurement, statistical characteristics evaluation and fatigue damage equivalency. Apply this road load data method procedure on 3 type of vehicle which represent conventional vehicle, BEV and HEV.

Proton exchange membrane fuel cell (PEMFC) system is expected to be the next generation vehicle powertrain. However, water management is still the main problem which directly affects the performance, reliability and durability in PEMFC system. To ensure the accurate water content estimation, High Frequency Resistance (HFR) is the most representative indicator in laboratory. The HFR is calculated by detecting the Alternating Current (AC) voltage response of fuel cell under the excitation of 1k Hz AC current. The voltage level of the AC excitation affects the measurement of HFR. Generally, 5mV AC excitation is used to measure HFR for the fuel cell with an output voltage between 0.6-1V. So, online HFR monitor is a big challenge for vehicle application, due to its low Signal/Noise Ratio (S/N), poor Electromagnetic Compatibility (EMC) environment, high common mode voltage and several hundred cells. In this work, an online fuel cell HFR monitor controller is developed.

The action of load on the component is crucial to evaluate the performance of durability. Another factor that affects fatigue life is the boundary conditions of the test specimen being tested by introducing unrealistic loads on the component of interest. The physical test is widely conducted in the laboratory. The fixture provides additional constraints on the test specimen as well as reaction forces to balance the test system [1]. The characteristics of the fixture involved in the test is important to analyze and assess the test results [2]. The impact of the reaction force of the fixture on the spindle-coupled axle road simulation test is presented in this article. A simplified 7-DoF (degrees of freedom) model is introduced to demonstrate the dynamic behavior of the vehicle. The influence on the internal load by the fixture has been analyzed. Followed by a more detailed MBS (multibodysystem) model to give a thorough understanding of the phenomenon.

As is known to all, the structure of the chassis has been greatly simplified as the application of in-wheel motor in electric vehicle (EV) and distributed control is allowed. The micro EV can alleviate traffic jams, reduce the demand for motor and battery capacity due to its small size and light weight and accordingly solve the problem that in-wheel motor is limited by inner space of the wheel hub. As a result, this type of micro EV is easier to be recognized by the market. In the micro EV above, two seats are side by side and the battery is placed in the middle of the chassis. Besides, in-wheel motors are mounted on the rear axle and only front axle retains traditional hydraulic braking system. Based on this driving/braking system, distribution of braking torque, system reliability and braking intensity is analyzed in this paper.

For distributed drive electric vehicles (DDEVs), the influence of the power ratio between the front and rear motors on the energy efficiency characteristics is investigated. The power-train systems of the DDEVs in this study are divided into two different power-train configurations. The first is with its front axle driven by wheel-side motors and the rear axle driven by in-wheel motors, and the second is with both the front and rear axles driven by in-wheel motors. The energy consumption simulation and analysis platform of the DDEV is built with Matlab/Simulink. The parameters of the key components are determined by the experiments to ensure the validity of the data used in simulation. At the same time, the vehicle’s average energy efficiency coefficient is defined to describe the energy efficiency characteristics of the power-train strictly. Besides, the control strategies for driving and braking of the DDEV based on energy efficiency optimization are presented.

In this paper, a speed tracking controller is designed for the All-terrain vehicles. The method of feedforward with state variable feedback based on conditional integrators is adopted by the proposed control algorithm. The feedforward is designed considering the influence of the road slope on the longitudinal dynamics, which makes the All-terrain vehicles satisfy the acceleration demand of the upper controller when it tracks the desired speed on the road with slope varying greatly. The road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimation requires an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. Based on the vehicle dynamic model and the nonlinear tire model, the method of conditional integration is used in the state variable feedback, which considers the saturation constraint of the actuator with the intention of preventing the divergent integral operation.

There are many electronic devices in electric vehicle (EV), making its electromagnetic compatibility (EMC) serious. Motor drive system is the main interference source of EV, whose electromagnetic interference (EMI) is much worse than conventional vehicle. In this paper, the motor drive system of EV was mainly researched, and a co-simulation method was proposed: control system and motor model were established with Matlab, and the equivalent circuit model of inverter and the cable model were established with Saber. By this way, a complete motor drive system model for conductive EMI was obtained. This modeling method can not only accurately establish the EMI sources and coupling paths, but can simulate the control strategy and operating conditions.

Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of Advanced Driver Assistance Systems (ADAS) and autonomous driving. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on centralized fusion strategy that the fusion center takes a unified track management. At First, vision sensor and radar are used to detect the target and to measure the range and the azimuth angle of the target. Then, the detections data from vision sensor and radar is transferred to fusion center to join the multi-target tracking with the prediction of current tracks.

This paper constructs the Accident Crash Scenarios(ACSs) classification system based on the traffic accident data collected by the traffic management department in a Chinses city from 2013 to 2015. The classification system selects four influenced variables on the basis of Critical Driving Scenarios(CDSs) in Naturalistic Driving Data. The proportions of each variable are analyzed, and all ACSs are divided into 48 scenarios. The highest proportion of nine ACSs are extracted from all 10596 ACSs, and the result shows the ACSs involved Car-Pedalcyclist occupy the top four scenarios, and the scenarios involved intersection situations are worth attention. Pedalcyclists include bicyclists, motorcyclists, tri-cyclists and electric bicyclists. Multivariate Logistic Regression(MLR) analysis is then used to study the ACSs involved the type of Car-Pedalcyclist.

To promote the development of automated vehicles (AVs), large scale of field operational tests (FOTs) were carried out around the world. Applications of naturalistic driving data should base on correlative scenarios. However, most of the existing scenario typologies, aiming at advanced driving assistance system (ADAS) and extracting discontinuous fragments from driving process, are not suitable for AVs, which need to complete continuous driving tasks. In this paper, a systematic scenario-typology consisting of four layers (from top to bottom: trip, cluster, segment and process) was first proposed. A trip refers to the whole duration from starting at initial parking space to parking at final one. The basic units ‘Process’, during which the vehicle fulfils only one driving task, are classified into parking process, long-, middle- and short-time-driving-processes. A segment consists of two neighboring long-time-driving processes and a middle or/and short one between them.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

The intersection is recognized as the most dangerous area because of the restricted road structures and indeterminate traffic regulations. Therefore, according to the Vehicle-to-everything (V2X) communication, Intelligent Transportation Systems (ITS), and Digital Twin data, we present a potential energy field method to establish the general characteristics of intersection traffic safety, evaluate the safety situation of intersection and assist intersection traffic participants in passing through the intersection safer and more efficient. The resulting potential energy field method is established by the contour line of traffic participants' potential energy, which is constructed as a superposition of disparate energies, such as boundary potential energy, body potential energy, and velocity potential energy. The intersection traffic safety is evaluated by the potential energy field characteristic of simultaneous intersection traffic participants.

The foundation of both advanced driving assistance system(ADAS) and automated driving (AD) is an accurate environment perception system(EPS). However, evaluation and test method of EPS are seldom studied. In this paper, naturalistic driving environment was studied and test scenarios for EPS under rear-end collision risk were proposed accordingly. To describe driving environment, a new concept named environment perception element(EPE) was first proposed in this paper, which refers to all the objects that the EPS must perceive during driving. Typical environment perception elements include weather and light conditions, road features, road markings, traffic signs, traffic lights, other vehicles, pedal cyclists and pedestrians and others. Driving behaviors collected in Shanghai, China were classified and rear-end collision risk scenarios were obtained and described using EPEs. Probability distribution of EPEs was therefore obtained.

Electromagnetic compatibility of electric vehicles is withstanding great challenges because of multiple ECU and actuators distributed in vehicle. At present, researches on electromagnetic characteristics of vehicle mainly focus on rectification based on the EMC experiment, which take much time and energy. Thus, this paper adopts the way of computer simulation studying the electromagnetism model and electromagnetic characteristics of electric vehicle with following procedure. Firstly, the equivalent model of wiring harness was deduced and built. Harnesses were divided into different groups according to terminal reflection property. The equivalent unit parameter matrix was calculated to build the equivalent wiring harness. The crosstalk and radiation cases were set to check the equivalent harness method.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.

A rapid path-planning algorithm that generates drivable paths for an autonomous vehicle operating in structural road is proposed in this paper. Cubic B-spline curve is adopted to generating smooth path for continuous curvature and, more, parametric basic points of the spline is adjusted to controlling the curvature extremum for kinematic constraints on vehicle. Other than previous approaches such as inverse kinematics, model-based prediction postprocess approach or closed-loop forward simulation, using the kinematics model in each iteration of path for smoothing and controlling curvature leading to time consumption increasing, our method characterized the vehicle curvature constraint by the minimum length of segment line, which synchronously realized constraint and smooth for generating path. And Differ from the path of robot escaping from a maze, the intelligent vehicle traveling on road in structured environments needs to meet the traffic rules.

The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image.

Smart electric vehicles need more accurate and more timely information as well as control than traditional vehicles, which depends on great environmental sensors such as millimeter-wave radar. In this way, the electromagnetic compatibility of whole vehicle would confront more serious challenges because of its high frequency range. Thus, this paper studies the electromagnetic distribution and electromagnetic interference suppression of smart electric vehicles with the followings. Firstly, the millimeter wave radar is modeled and optimized. Micro strip patch antenna, with small size, light mass and low cost, is used as array element of antenna. Millimeter wave radar is modeled and simulated step by step from array element to line array to planar matrix. Then the Cross Shape - Uniplanar Compact - Electromagnetic Band Gap (CS-UC-EBG) structure is deployed to optimize its electromagnetic characteristics, based on finite time domain difference model theory.