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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

Conventional viscous shock absorbers, in parallel with suspension springs, passively dissipate the excitation energy from road irregularity into heat waste, to reduce the transferred vibration which causes the discomfort of passengers. Energy-harvesting shock absorbers, which have the potential of conversion of kinetic energy into electric power, have been proposed as semi-active suspension to achieve better balance between the energy consumption and suspension performance. Because of the high energy density of the rotary shock absorber, a rotational energy-harvesting shock absorber with mechanical motion rectifier (MMR) is used in this paper. This paper presents the assessment of vehicle dynamic performance with the proposed energy-harvesting shock absorber in braking process. Moreover, a PI controller is proposed to attenuate the negative effect due to the pitch motion.

The performance of electric vehicles could be enhanced by more flexible drivetrain configurations combined with advanced control methods. Based on four wheel independent driving and front and rear axle modular steering configuration, which was proposed by our research group last year, the problem of slippery under close-to-limit conditions are further discussed and simulated. A new torque vectoring method based on obtainable parameters and variables in real driving situations is introduced to reduce the sideslip when turning on low friction surfaces or with high speed. This method is developed from a comprehensive index, which reflects the stability and maneuverability, by adding additional torques when stability could not be compensated enough by basic torque vectoring. Besides, an improvement of adding a simu-Torsen differential mechanism is made to the model of the vehicle, which enables another control method with the same purpose as before.

The vehicle pull problem is very important to driving safety. Major factors that may cause the pull problem related to tire include variations of geometric dimension (e.g. RPK) and stiffness (e.g. cornering stiffness, aligning stiffness), plysteer and conicity. In previous research, the influencing mechanism of these factors was well studied. But in fact, vehicle pull problem caused by tire is probabilistic. When we assemble four tires onto the car, there could be 384 different assembly arrangements. If there are significant differences among these four tires, there will also be significant differences in the influence of different tire assembly schemes on vehicle pull, which has not been systematically discussed in previous studies. If we want to evaluate the pull performance of all these arrangements by vehicle test, it will be a time consuming process which will take almost 24 working days, along with a high test expense.

Artificial intelligence systems are highly accepted as a technology to offer an alternative way to tackle complex and non-linear problems. They can learn from data, and they are able to handle noisy and incomplete data. Once trained, they can perform prediction and generalization at high speed. The aim of the present study is to propose a novel approach utilizing the adaptive neuro-fuzzy inference system (ANFIS) and the fuzzy clustering method for automotive ride performance estimation. This study investigated the relationship between the automotive ride performance and relative parameters including speed, spring stiffness, damper coefficients, ratios of sprung and unsprung mass. A Takagi-Sugeno fuzzy inference system associated with artificial neuro network was employed. The C-mean fuzzy clustering method was used for grouping the data and identifying membership functions.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

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.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

To reduce the interference and conflict of human-machine cooperative control, lighten the operation workload of drivers, and improve the friendliness and acceptability of intelligent vehicles, a personalized human-machine cooperative lane-change trajectory tracking control method was proposed. First, a lane-changing driving data acquisition test was carried out to collect different driving behaviors of different drivers and form the data pool for the machine learning method. Two typical driving behaviors from an aggressive driver and a moderate driver are selected to be studied. Then, a control structure combined by feedforward and feedback control based on Long Short Term Memory (LSTM) and model-based optimum control was introduced. LSTM is a machine learning method that has the ability of memory. It is used to capture the lane-changing behaviors of each driver to achieve personalization. For each driver, a specific personalized controller is trained using his driving data.

Lane-changing systems have been developed and applied to improve environmental adaptability of advanced driver assistant system (ADAS) and driver comfort. Lane-changing control consists of three steps: decision making, trajectory planning and trajectory tracking. Current methods are not perfect due to weaknesses such as high computation cost, low robustness to uncertainties, etc. In this paper, a novel lane changing control method is proposed, where lane-changing behavior is analogized to parallel parking behavior. In the perspective of host vehicle with lane-changing intention, the space between vehicles in the target adjacent lane can be regarded as dynamic parking space. A decision making and path planning algorithm of parallel parking is adapted to deal with lane change condition. The adopted algorithm based on rules checks lane-changing feasibility and generates desired path in the moving reference system at the same speed of vehicles in target lane.

With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance. This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object.

The explosion of big data has created challenges for both cloud-based systems and Autonomous Vehicles (AVs) in data collection and management. The same challenges are now being realized in developing databases for integrated sensors, streaming, real-time and on-demand services in AVs. With just one AV expecting to generate over 30 Terabytes of data a day, modern NoSQL databases provide opportunities to horizontally scale AV data seamlessly. NoSQL provides solutions designed to accommodate a wide variety of data models such as, key-value, document, column and graph databases. Key-value stores are by nature scalable, fast processing, and distribute horizontally. These databases are tasked with handling several data types including IoT, radar, lidar, ultra-sonic sensors, GPS, odometry, and sensor data while providing streaming and real-time services. NoSQL can store and utilize structured, semi-structured, and unstructured data necessary for multimedia storage needs.

The Electromechanical Brake Booster system (EMBB) integrates active braking and energy recovery and becomes a novel brake-by-wire solution that substitutes the vacuum booster. While the intelligent unmanned vehicle is in unstable state, the EMBB can improve the vehicle yaw stability more quickly and safely. In this paper, a new type of integrated EMBB has been designed, which mainly includes two parts: servo motor unit and hydraulic control unit. Aiming at the dynamic instability problem of intelligent unmanned vehicle, a three-layer vehicle yaw stability control structure including decision layer, distribution layer and execution layer is proposed based on integrated EMBB. Firstly, the decision layer calculates the ideal yaw rate and the side slip angle of the vehicle with the classic 2DOF vehicle dynamics model. The boundary of the stable region is determined by the phase plane method and the additional yaw moment is determined by the feedback PI control algorithm.

A high-precision steady state tire model is critical in the tire and vehicle matching research. For the moment, the popular Magic Formula model is an empirical model, which requires the pure and combined test data to identify the model parameters. Although MTS Flat-trac is an efficient tire test rig, the long test period and high test cost of a complete tire model tests for handling are yet to be solved. Therefore, it is necessary to explore a high accuracy method for predicting tire complex mechanical properties with as few test data as possible. In this study, a method for predicting tire combined slip characteristics from pure cornering and pure longitudinal test data has been investigated, and verified by comparing with the test data. Firstly, the prediction theory of UniTire model is introduced, and the formula for predicting combined slip characteristics based on constant friction coefficient is derived.

This paper developed a control system for the auxiliary power unit (APU) in off-road series hybrid electric special vehicle. A control system configuration was designed according to the requirements of the high voltage system in series hybrid electric special vehicle. Then optimal engine operating areas were defined. A gain scheduling engine speed PI controller was designed based on these areas. A closed loop voltage regulator was designed for the synchronous generator. The proposed control system was first validated on an APU control test bench. The test results showed the control system guaranteed the diesel APU good dynamic response characteristics while remaining stable output voltage. Finally, the APU control system was implemented on a diesel APU in an off-road series hybrid electric vehicle and a road test was conducted. The road test results showed the APU control system promised good performance in both vehicle dynamics and vehicle high voltage system.

A tire force estimation algorithm is proposed for vehicle dynamic stability control (DSC) system to protect the vehicle from deviation of the normal dynamics attitude and to realize the improved dynamics stability in limited driving conditions. The developed algorithm is based on the theoretical analysis of all the subsystems of the active brake control in DSC system and modulation in DSC, and the robustness is achieved by a compensation method using nonlinear filter in the real time control. The software-in-loop simulation using Matlab/AMEsim and the ground test in the real car show the validation of this method.

This paper sets up a simplified dynamic model for simulating the yaw/roll stability of heavy tractor-semitrailer using Matlab/Simulink. A linear quadratic regulator (LQR) based on partial-state feedback controller is used to optimize the roll stability of the vehicle. The control objective for optimizing roll stability is to be reducing the lateral load transfer rate while keeping the suspension angle less than the maximum allowable angle. The simulation result shows that the LQR controller is effective in the active roll stability control of the heavy tractor-semitrailer.

Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

In order to improve the tractive ability, steering capability and directional stability, etc. of automated mechanical transmission (AMT) vehicles running on the wet and slippery road, the anti-slip regulation (ASR) technologies for AMT vehicles are developed. The significance of ASR for AMT vehicles is introduced; a road friction recognition method based on the deceleration of driving wheels is investigated; a fuzzy anti-slip control system based on adjustment of engine torque is developed and the corresponding experimental verification is conducted. The experimental results denote that the proposed method is effective to eliminate the excessive slip when the AMT vehicle travels on the low friction road.

Based on the simulation results of tire rolling over perpendicular cleats by MPTM model, in present paper, a series of simulation results of tire rolling over oblique cleats with different angles are given. For that, the Modal Parameter Tire Camber property Model is established. For the appraisement of comparison between simulation and experimental results a problem concern the validation test is pointed out. In the end, simulation results of tire rolling over a series of continuous cleats are given.