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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.

The current global chassis control (GCC) frequently makes use of decoupled control methods which depend on driving condition partition and simple rule-based vertical force distribution, and are insufficient to obtain optimal vehicle dynamics performance. Therefore, a novel hierarchical global chassis control system for a distributed electric vehicle (DEV), which is equipped with four wheel driving/steering and active suspension systems, is developed in this paper. The control system consists of three layers: in the upper layer, the desired forces/moments based on vehicular driving demands are determined; in the middle layer, a coordinated control method of longitudinal/lateral/vertical tire forces are proposed; in the lower layer, the driving/steering/suspension control is conducted to realize each distributed tire force.

This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

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.

As a novel assist actuator of brake system, the electromechanical brake (EMB) booster has played a significant role in the battery electric vehicles and automatic driving vehicles. It has advantages of independent to vacuum source, active braking, and tuning pedal feeling compared with conventional vacuum brake booster. In this article, a novel EMB booster system is proposed, which is consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction by gears and ball screw, a servo body, and a reaction disk. Together with the hydraulic control unit, it has two working modes: active braking for automatic drive and passive braking for driver intervention. The structure and work principle of the electric brake booster system is first introduced. The precise control from pedal force to hydraulic pressure is the key for such a power-assisted brake actuator. We translate the control problem of force feedback control to position tracking control.

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.

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.

Vehicle needs suspension and steering systems with different features to fit different driving conditions. In normal straight driving condition, soft suspension and heavy steering systems are needed to achieve better ride comfort and straight line driving stability; in turning conditions, hard suspension and lightweight steering systems are needed to get better handing stability. The semi-active suspension system with Magneto-Rheological dampers can improve the ride comfort and handling performance of vehicle. Electrical power steering system is developed rapidly due to its portable and flexible operations as well as stable steering performance.

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.

Supercapacitor has the merits of low resistance and long lifecycle ability. When combined with battery, they can alleviate the burthen of battery, increase the battery's working efficiency and prolong its lifecycle. This paper introduces a control architecture based on balancing of SOC and algorithm based on fuzzy logic, Aiming at the two different cycles that have sufficient and insufficient energy that can be recovered from braking unlikely, this paper puts forward the methods of on-line adjusting fuzzy control parameters. Consequently, simulation was performed,and the results validate the effective adapting capacity of the control logic under different driving cycles.

The motor errors of the four-wheel independent driving electric vehicles was studied, and classified as steady errors and dynamic errors. In the paper, the forward compensation control and the PID control strategy were respectively applied to compensate for the errors. The electric vehicles straight-line running stability caused by the motor errors was discussed, the PID control method based on BP neural net work was presented to co-ordinate torque of the wheels. The result of the simulation on the 7 degrees of freedom vehicle model showed that control method improved the straight-line running ability of the vehicle.

The incomplete combustion and misfire of diesel engine during starting result in unwanted white smoke. The histories of combustion and emission in different phases under different start conditions were studied in this paper. The optimization of the fuel injection control strategy under start conditions was performed. When the diesel engine is started under low temperature, the control strategy adapted to start the engine with a certain constant fuel mass injected per cycle, there may be misfire cycles in the initial period or in the transitional process, which is mainly caused by the mismatch between the fuel mass injected per cycle and the instantaneous engine speed. Therefore, an optimized control strategy was put forward, namely, the engine starts with high fuel mass injection in the first several cycles and then decreases step by step during the transitional period until it operates at idle condition. This strategy was validated to decrease significantly the misfire cycles.

A life-cycle assessment (LCA) has been developed to help compare the economic, environmental and energy (EEE) impacts of converting coal to automotive fuels in China. This model was used to evaluate the total economic cost to the customer, the effect on the local and global environments, and the energy efficiencies for each fuel option. It provides a total accounting for each step in the life cycle process including the mining and transportation of coal, the conversion of coal to fuel, fuel distribution, all materials and manufacturing processes used to produce a vehicle, and vehicle operation over the life of the vehicle. The seven fuel scenarios evaluated in this study include methanol from coal, byproduct methanol from coal, methanol from methane, methanol from coke oven gas, gasoline from coal, electricity from coal, and petroleum to gasoline and diesel. The LCA results for all fuels were compared to gasoline as a baseline case.

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.

Numerous modern military and commercial vehicles rely on portable, battery-powered sources for electric energy. Due to their highly specialized functions these vehicles are typically custom-designed, produced in limited numbers, and expensive. To mitigate the power system's contribution to these undesirable characteristics, this paper proposes a modular power system architecture consisting of “smart” power battery units (SPUs) that can be readily interconnected in numerous ways to provide distributed and coordinated system power management. The proposed SPUs contain a battery power source and a power electronics converter. They are compatible with multiple battery chemistries (or any energy storage device that can produce a terminal voltage), allowing them to be used with both existing and future energy storage technologies.

Coordinated EGR-VNT control based on the intake air mass observer is presented in this paper to deal with the transient AFR control of turbocharged diesel engine. The air mass model embedded in the observer is a Takagi-Sugeno fuzzy neural network trained with transient simulation results. It can predict the charged fresh air mass entering the cylinder. In a high load region, when EGR is not effective, the coordinated EGR-VNT control will also bring benefits to the transient air-fuel-ratio control. The simulation results of TDI engine model verify that the transient control strategy will allow a better control of the intake air mass, and thus improve air-fuel-ratio control and reduce NOx emission in transients.

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.

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.