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For fracture cracks that occurred in the tight coupling exhaust manifold durability test of a four-cylinder gasoline engine with EGR channel, causes and solutions for fracture failure were found with the help of CFD and FEA numerical simulations. Wall temperature and heat transfer coefficient of the exhaust manifold inside wall were first accurately obtained through the thermal-fluid coupling analysis, then thermal modal and thermoplastic analysis were acquired by using the finite element method, on account of the bolt pretightening force and the contact relationship between flange face and cylinder head. Results showed that the first-order natural frequency did not meet the design requirements, which was the main reason of fatigue fracture. However, when the first-order natural frequency was rising, the delta equivalent plastic strain was increasing quickly as well.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO).

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables.

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle.

With the rapidly developing automotive industry and stricter environmental protection laws and regulations, lightweight materials, advanced manufacturing processes and structural optimization methods are widely used in body design. Therefore, in order to evaluate and improve the pedestrian protection during a collision, this paper presents an impact simulation modeling and structural optimization method for a sport utility vehicle engine hood made of carbon fiber reinforced plastic (CFRP). Head injury criterion (HIC) was used to evaluate the performance of the hood in this regard. The inner panel and the outer panel of CFRP hood were discretized by shell elements in LS_DYNA. The Mat54-55 card was used to define the mechanical properties of the CFRP hood. In order to reduce the computational costs, just the parts contacted with the hood were modeled. The simulations were done in the prescribed 30 impact points.

Gradient based topology optimization method is difficult used to optimization of crashworthiness structures due to the expensive computational cost of sensitivity analysis and complex nonlinear behaviors (geometric nonlinearity, material nonlinearity and contact nonlinearity) of structures during a collision. Equivalent static loads (ESLs) method is one of the methods for nonlinear dynamic response optimization. However, this method ignores the material nonlinearity. Thus this paper proposes an improved topology optimization method for crashworthiness structure based on a modified ESLs method. A new calculation of ESLs considering material nonlinearity is proposed. The improved ESLs method is employed to transform the nonlinear dynamic response optimization into a nonlinear static response optimization with multiple load cases. Each element in the design domain is assigned with a design variable.

At present, many electric vehicles are often equipped with only a single-stage final drive. Although the single-stage speed ratio can meet the general driving requirements of electric vehicles, if the requirements of the maximum speed and the requirements for starting acceleration or climbing are met at the same time, the power demand of the drive motor is relatively large, and the efficient area of the drive motor may be far away from the operating area corresponding to daily driving. If the two-speed automatic transmission is adopted, the vehicle can meet the requirements of maximum speed, starting acceleration and climbing at the same time, reduce the power demand of the driving motor, and improve the economy under certain power performance. This is especially important for medium and large vehicles.

Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed.

Driving comfort is one of the most important indexes for automobile comfort. Driving posture comfort is closely related to the drivers' joint angles and joint torques. In present research, a new method is proposed to identify the most comfortable driving posture based on studying the relation between drivers' joint angles and joint torques. In order to truly reflect a driving situation, the accurate human driving model of 50 percent of the size of Chinese male is established according to the human body database of RAMSIS firstly. Biomechanical model based on accurate human driving model is also developed to analyze and obtain dynamic equations of human driving model by employing Kane method. The joint torque-angle curves of drivers' upper and lower limbs during holding wheel or pedal operation can be obtained through dynamic simulation in the MATLAB. Through curve-fitting analysis, the minimum joint torque of a driver' limb and the optimal joint angel can be found.

With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial.

On-road testing of driving performance of the urban bus was carried out, and a representative urban bus driving cycle was developed after on-road testing, according to the test results. Then, the vehicle simulation software AVL CRUISE was used to simulate the dynamic behavior of the urban bus. It involves the simulation of complete drive train system and the driver behavior. The model is validated by comparing the results of the simulation to the results of the field test. Then the developed driving cycle is evaluated by fuel consumption resulted from the simulation and engine bench test on fuel economy.

Dual-Clutch-Transmission (DCT) is one kind new automatic transmission which has double clutch structure. The most important unit of DCT is Transmission-Control-Module (TCM).In the development process of TCM, simulation is an important research tools. We have analyzed the DCT principle of work, established its mathematical model, created the charge and discharge oil models of typical wet dual clutch transmission, established the control logic to unify and separate double clutch in turn, and also designed out the shift control using fuzzy control using MATLAB/Simulink software. Utilizing engine model, driver model, the DCT model, the TCM model, the vehicle model, established the vehicle simulation model, and implemented simulation; Result indicated that, the established model can correctly reflect the torque and speed change when shifted gears and can correctly realize the automatic shift gears.

Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact.

The closed cabin temperature is anticipated to be cooled down when it is a bit hot inside the driving car. The traditional air-condition lowers the cabin temperature by frequently switching the status of the compressor, which increases the engine’s parasitic power and shortens the compressor’s service-life. The semiconductor auxiliary cooling system with the properties of no moving parts, high control precision and quick response has the potential to assist the on-board air-condition in modulating the cabin temperature with relative small ranges. Little temperature differences between the cabin and the outside environment means that the system energy consumption to ensure the occupant comfort is relatively low and the inefficiency could be made up by the renewable energy source.

Automotive Front Lighting System(AFS) can receive the steering signal and the vehicular speed signal to adjust the position of headlamps automatically. AFS will provide drivers more information of front road to protect drivers safe when driving at night. AFS works when there is a steering signal input. However, drivers often need the front road's information before they turn the steering wheel when vehicles are going to go through a sharp corner, AFS will not work in such a situation. This paper studied how to optimize the working time of AFS based on GIS (Geographic Information System) and GPS(Geographic Information System) to solve the problem. This paper analyzed the process of the vehicle is about to go through a corner. Low beams and high beams were discussed respectively.

The drivers' hysteretic perception to surrounding environment will affect vehicular fuel economy, especially for the heavy-duty vehicles driving under complex conditions and long distance in mountainous areas. Unreasonable acceleration or deceleration on the slope will increase the fuel consumption. Improving the performance of the engine and the transmission system has limited energy saving potential, and most fuel-efficient driving assistant systems don't consider the road conditions. The main purpose of this research is to introduce an economic driving scheme with consideration of the prestored slope information in which the vehicle speed in mountainous slopes is reasonably planned to guide the driver's behavior for reduction of the fuel consumption. Economic driving optimization algorithm with low space dimension and fast computation speed is established to plan accurate and real-time economic driving scheme.

In this paper a new pressure control method of a modified accumulator-type Electro-hydraulic Braking System (EHB) is proposed. The system is composed of a hydraulic motor pump, an accumulator, an integrated master cylinder, a pedal feel simulator, valves and pipelines. Two pressurizing modes are switched between by-motor and by-accumulator to adapt different pressure boost demands. A differentiator filtering raw sensor signal and calculating pedal speed is designed. By using the pedal feel simulator, the relationship between wheel pressures and brake force is decoupled. The relationships among pedal displacement, pedal force and wheel pressure are calibrated by experiments. A model-based PI controller with predictor is designed to lower the influences caused by delay. Moreover, a self-tuning regulator is introduced to deal with the parameter’s time-varying caused by temperature, brake pads wearing and delay variation.

This research is based on the Controller Area Network (CAN) bus, and briefly analyzed its communication protocol with reference to the layered model of Open System Interconnect Reference Model (OSI). Subsequently, a data acquisition system was designed and developed including a Vehicle Communication Interface (VCI) and a laptop. After the overall architecture was built, the communication mechanism of the VCI was studied. Furthermore, the lap top app was built using the layered design followed by the implementation of a scheme for data collection and experimentation involving the test driving of a real car on road. Finally, the driving style was identified by means of fuzzy reasoning and solving ambiguity based on fuzzy theory; via training the acceleration sample and forecast using the excellent learning and generalization ability of Support Vector Machine (SVM) for high-dimensional, finite samples.

In this paper, we propose a method of dynamics simulation and analysis based on superelement modeling to increase the efficiency of dynamics simulation for vehicle body structure. Using this method, a certain multi-purpose vehicle (MPV) body structure was divided into several subsystems, and the modal parameters and frequency response functions of which were obtained through superelement condensation, residual structure solution, and superelement data restoration. The study shows that compared to the traditional modeling method, the computational time for vehicle body modal analysis can be reduced by 6.9% without reducing accuracy; for the purpose of structural optimization, the computational time can be reduced by 87.7% for frequency response analyses of optimizations; consistency between simulation and testing can be achieved on peak frequency points and general trends for the vibration frequency responses of interior front row floors under accelerating conditions.