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This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.

The temperature management of the proton exchange membrane fuel cell cooling system is very important to the stability and life of the engine. The size and cycle switching of the cooling system and the rapid temperature rise of the system are inseparable from the thermostat. In this paper, a simulation model of the fuel cell cooling system is built for a 30KW fuel cell system, and its accuracy is verified by experiments. The temperature fluctuation of the system in the confluence mode is mainly studied, and the error is about 0.9 °C. The influence of the converging mode of the thermostat on the temperature of the cooling system is researched through the simulation platform. Based on the simulation model, the influence of thermostats with different external environments and opening degrees on the size cycle switching of cooling system is predicted, which provides optimization and guidance for the system control strategy.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

The accuracy and range of chassis control for a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in observer-based EVs control for improving road handling and ride comfort, is a challenging task for the IWM-driven vehicle system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely acquire movement state and chose the reasonable observer-based control algorithm for IWM-driven EVs become a hot topic in both academia and industry. Simultaneously, uncertainty is always existing, e.g., varying road excitation, variable system parameters or nonlinear structure. Meanwhile, the coupling effects between the non-ideal IWM actuator and vehicle are ignored under the assumption of an ideal actuator.

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels.

With the increasingly complex traffic environment, the vehicle AEB system needs to go through a large number of testing processes, in order to drive more safely on the road. For speeding up the development process of AEB and solve the problems of long cycle, high cost and low efficiency in AEB testing, in this paper, a millimeter wave radar turntable is built, and a high-precision detection algorithm of turntable encoder is designed, at the same time, a test method of vehicle AEB based on the detection data of radar turntable encoder is designed. The verification results show that methods described in this paper can be used to develop the vehicle AEB test algorithm efficiently.

Hydrogen safety is of great importance in proton exchange membrane fuel cell (PEMFC) systems. Anode pressure control has become a focus point in recent years. The differential pressure between anode and cathode in PEMFC system needs to be carefully controlled under a suitable threshold. In practice, the anode pressure is usually controlled about 20–30kPa higher than the cathode pressure to minimize nitrogen crossover and improve cell stability. High differential pressure could lead to irreversible damage in proton exchange membrane. PID control was the dominant method to control the anode pressure in the past. However, the anode pressure’s fluctuation when hydrogen mass flow suddenly changes is a long-term challenge. As the requirements of control precision are increasingly high, the traditional PID control needs to be improved. Several new control algorithms are presented in recent researches, however, mostly are theoretical and experimental.

In order to solve such problems that the millimeter-wave radar is of large computation, poor robustness and low precision of the target tracking algorithm, this paper presents an algorithmic framework for millimeter-wave radar tracking of target-vehicles. The target measurement information outside the millimeter- wave radar detection range is eliminated by the data plausibility judgment method based on the millimeter-wave radar detection parameters. Target clustering is made using Manhattan distance, to eliminate clutter interference and cluster multiple target measurements into one. The data association is made by use of nearest neighbor to determine the correspondence between information received measured by the radar and the real target. The vehicle is the key detection target of the vehicle millimeter-wave radar during road driving.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

The fatigue load spectrum of the physical proving ground is the necessary input for fatigue life analysis of vehicle parts and components. It is usually obtained by Road Load Data Acquisition (RLDA) and loads decomposition using multi-body dynamics tools. Virtual Proving Ground (VPG) methodology is gradually replacing this technical strategy. The belgian road is the typical event in durability test, in this paper, the flexible body and FTire model are applied to the vehicle multi-body dynamics model in order to improve the simulation accuracy. The result shows that all the wheel center force, shock absorber displacement and axial force acquired by VPG simulation have excellent correlation with real vehicle measured data. It is also proved that the virtual proving ground technology is a reliable and effective method to obtain the fatigue load spectrum in the early stage of development.

Intelligent and connected vehicle (ICV) and new energy vehicle (NEV) will be two important directions of the automotive technology in the future, and the coordinated development of these two directions reflects relevantly the higher requirements put forward by nowadays society and people. Through the use of intelligent and connected technology (ICT), NEVs can exchange various traffic information data with the outside world (e.g. other running vehicles, road infrastructure, internet, etc.) in real time, which is so-called Vehicle to Everything (V2X). Based on the further analysis of the mutual traffic information, the vehicles can identify the current driving conditions and predict the future driving conditions effectively, which can realize the real time optimization of the energy management strategies (EMSs) of vehicles’ powertrain system, so as to meet the driving requirements of vehicles under different driving conditions.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

The paper analyzes the mechanism of automobile millimeter wave radar noise, this paper does not study radar noise from the angle of signal processing, but from the level of false detection and missed detection, at the same time, the noise mechanism is modeled and verified. Firstly, the purpose and significance of the research of radar vehicle noise are described, and then, we summarize and outline the macro phenomenon and the specific characteristics of the automobile millimeter wave radar noise.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

In this article, the regenerative braking system is designed, which can realize the torque allocation between electric braking and hydraulic braking, where the cost function designed in this article considers factors of braking torque following effect, energy regenerative power, and hydraulic braking consumed power. In addition, a complete anti-lock braking system (ABS) is designed, which is based on regenerative braking. With the optimal slip ratio as control target, target wheel speed, control wheel speed, braking torque control strategy, and enable/disenable control logic of ABS are determined. By MATLAB/Simulink-DYNA4 co-simulation and real vehicle test, the feasibility and applicability of ABS based on regenerative braking are verified, under the condition of small severity of braking.

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.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.

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.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

CORUN CHS has recently developed a new power-split hybrid transmission platform CHS-HT2800, which has added 2 clutches compared to CHS-HT1800. With involvement of 2 E-motors CHS-HT2800 has 9 forward gears, including 3 electric forward gears and 6 hybrid forward gears. Among the 6 hybrid gears 4 gears are fixed-ratio gears and the remaining 2 gears are eCVT gears. This paper describes a strategy, designed for CHS-HT2800, how to select the optimal mode, the optimal gear and the optimal work point of the engine to achieve the best economy performance in the real traffic. Because it is universally accepted, that the WLTC cycle can represent real world vehicle operation on urban and extra-urban roads, motorways, and freeways. The introduced strategy will be validated with the help of the WLTC cycle.