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Vehicle weight reduction has become one of the viable solutions to ever-growing energy and environmental crisis. In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affects the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. For the purpose of constructing and correcting the bias in RSMs, scheduling Design of Experiments (DOEs) must be conducted properly. This paper develops a method to arrange DOEs in order to build RSMs with high quality, considering the influence of input uncertainty.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A coupled magnetic-thermal model is established to study the reason for the damage of the starter motor, which belongs to the idling start-stop system of a city bus. A finite element model of the real starter motor is built, and the internal magnetic flux density nephogram and magnetic line distribution chart of the motor are attained by simulation. Then a model in module Transient Thermal of ANSYS is established to calculate the stator and rotor loss, the winding loss and the mechanical loss. Three kinds of losses are coupled to the thermal field as heat sources in two different conditions. The thermal field and the components’ temperature distribution in the starting process are obtained, which are finally compared with the already-burned motor of the city bus in reality to predict the damage. The analysis method proposed is verified to be accurate and reliable through comparing the actual structure with the simulation results.

Vehicle hood design is a typical multi-disciplinary task. The hood has to meet the demands of different attributes like safety, dynamics, statics, and NVH (Noise, Vibration, Harshness). Multi-disciplinary optimization (MDO) of vehicle hood at early design phase is an efficient way to support right design decision and avoid late-phase design changes. However, due to lacking in CAD models, it is difficult to realize MDO at early design phase. In this research, a new method of design and optimization is proposed to improve the design efficiency. Firstly, an implicit parametric hood model is built to flexibly change shape and size of hood structure, and generate FE models automatically. Secondly, four types of stiffness analysis, one type of modal analysis, together with pedestrian head impact analysis were established to describe multi-disciplinary concern of vehicle hood design.

The dynamic trajectory planning problem for automatic vehicles in complex traffic scenarios is investigated in this paper. A hierarchical motion planning framework is developed to complete the complex planning task. An improved dangerous potential field in the curvilinear coordinate system is constructed to describe the collision risk of automatic vehicles accurately instead of the discrete Gaussian convolution algorithm. At the same time, the driving comfort is also considered in order to generate an optimal, smooth, collision-free and feasible path in dynamics. The optimal path can be mapped into the Cartesian coordinate system simply and conveniently. Furthermore, a velocity profile considering practical vehicle dynamics is also presented to improve the safety and the comfort in driving. The effectiveness of the proposed dynamic trajectory planning is verified by numerical simulation for several typical traffic scenarios.

Accident statistics have shown that older and obese occupants are less adaptable to existing vehicle occupant restraint systems than ordinary middle-aged male occupants, and tend to have higher injury risk in vehicle crashes. However, the current research on injury mechanism of aging and obese occupants in vehicle frontal impacts is scarce. This paper focuses on the optimization design method of occupant restraint system parameters for specific body type characteristics. Three parameters, namely the force limit value of the force limiter in the seat belt, pretensioner preload of the seat belt and the proportionality coefficient of mass flow rate of the inflator were used for optimization. The objective was to minimize the injury risk probability subjected to constraints of occupant injury indicator values for various body regions as specified in US-NCAP frontal impact tests requirements.

The high strength-weight ratio and high damping capability of Magnesium alloys implies significant potentials for improving fuel efficiency and vehicle performance with the use Mg wheels. In this paper, a brief review is given of the current state of art in Mg wheel production, followed by a summary of the mechanical and casting properties of Mg alloys. The difficulties that hinder the wide use of Mg wheels are discussed. The R&D activities in China in the fields of Mg wheel design and casting are described. The focus of this paper is on the design and the development of a new squeeze casting process that makes it feasible to produce high-quality Mg wheels with cost efficiency. Finally, the expected commercial use of Mg wheels in the near future in Chinese motorbikes is outlined.

This paper proposed a two-section trajectory planning algorithm. In this trajectory planning, sigmoid function is adopted to fit two tangent arcs to meet limited parking spaces by reducing the radius of turning. Then the transverse preview model is established and the path tracking errors including distance error and angle error are estimated. The weight coefficient is considered to distribute the impact factor of traverse distance error or traverse angle error in the total error. The fuzzy controller is designed to track the two-section trajectory in autonomous intelligent parking system. The fuzzy controller is developed due to its real-time and robustness in the parking process. Traverse errors and its first-order derivative are selected as input variables and the outer wheel steering angle is selected as the output variable in fuzzy controller. They are also divided into seven fuzzy sets. Finally, forty rules are decided to achieve effective trajectory tracking.

Noise-vibration-harshness (NVH) design optimization problems have become major concerns in the vehicle product development process. The Body-in-White (BIW) plays an important role in determining the dynamic characteristics of vehicle system during the concept design phase. Finite Element (FE) models are commonly used for vehicle design. However, even though the speed of computers has been increased a lot, the simulation of FE models is still too time-consuming due to the increase in model complexity. For complex systems, like vehicle body structures, the numerous design variables and constraints make the FE simulations based optimization design inefficient. This calls for the development of a systematic and efficient approach that can effectively perform optimization to further improve the NVH performance, while satisfying the stringent design constraints.

Several surrogate models such as response surface model and radial basis function and Kriging models are developed to speed the optimization design of vehicle body and improve the vehicle crashworthiness. The error analysis is used to investigate the accuracy of different surrogate models. Furthermore, the Kriging model is used to fit the model of B-pillar acceleration and foot well intrusion. The response surface model is used to fit the model of the entire vehicle mass. These models are further used to calculate the acceleration response in B-pillar, foot well intrusion and vehicle mass instead of the finite element model in the optimization design of vehicle crashworthiness. A multi-objective optimization problem is formulated in order to improve vehicle safety performance and keep its light weight. The particle swarm method is used to solve the proposed multi-objective optimization problem.

Collaborative optimization is an important design tool in complex vehicle system engineering. However, there are many problems yet to be resolved when applying the conventional collaborative optimization method in vehicle body weight reduction, such as convergence difficulties and low optimization efficiency. To solve these problems, a maximum incompatibility constrained collaborative optimization method is proposed in the paper. First, the 1-norm equality constraints expression of the system level is used to replace the traditional 2-norm inequality constraints. Then, a maximum incompatibility is selected from modified inequality constraints to improve optimization efficiency. Finally, an overall compatibility constraint is introduced to decrease the influence caused by the initial point. A mathematical example is used to verify the effectiveness and stability of the proposed method.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

Surrogate model based design optimization has been widely adopted in automotive industry. Hybrid surrogate model with multiple component surrogates is considered to be a better choice when simulating highly non-linear responses in vehicle crashworthiness analysis. Currently, the number of component surrogates has to be decided before-hand when constructing of a hybrid surrogate model. This paper conducts a comparative study on the performances of three popular hybrid modeling methods including heuristic computation strategy, and two kinds of optimal weighted surrogates. The effects of samples size on the number of individual surrogates that should be included into the final hybrid surrogate models for crashworthiness responses are investigated. Different hybrid modeling techniques and multiple validation criteria are evaluated. Some observations and conclusions on the selection of component surrogates in hybrid surrogate modeling are given in the end.

Finite element (FE) models are commonly used for automotive crashworthiness design. However, even with increasing speed of computers, the FE-based simulation is still too time-consuming when simulating the complex dynamic process such as vehicle crashworthiness. To improve the computational efficiency, the response surface model, as the surrogate of FE model, has been widely used for crashworthiness optimization design. Before introducing the surrogate model into the design optimization, the surrogate should satisfy the accuracy requirements. However, the bias of surrogate model is introduced inevitably. Meanwhile, it is also very difficult to decide how many samples are needed when building the high fidelity surrogate model for the system with strong nonlinearity. In order to solve the aforementioned problems, the application of a kind of surrogate optimization method called Efficient Global Optimization (EGO) is proposed to conduct the crashworthiness design optimization.

Design optimization methods are widely used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges is to search for the optimal design in an efficient manner. For complex design and optimization problems such as automotive applications, optimization algorithms work better if the initial searching points are within or close to feasible domains. In this paper, the k-means clustering algorithm is exploited to identify sets of reduced feasible domains from the original design space. Within the reduced feasible domains, the optimal design can be obtained efficiently. A mathematical example and a vehicle body structure design problem are used to demonstrate the effectiveness of the proposed method.

According to driving scenarios, intelligent vehicle is mainly applied on urban driving, highway driving and close zone driving, etc. As one of the most valuable developments, automated highway driving has great progress. This paper focuses on automated highway driving decision, and considering decision efficiency and feasibility, a hierarchical motion planning algorithm based on dynamic programming was proposed, and simultaneously, road coordinate transformation methods were developed to deal with complex road conditions. At first, all transportation user states are transformed into straight road coordinate to simplify modeling and planning, then a set of candidate paths with Bezier form was developed and with the help of obstacles motion prediction, the feasible target paths with collision-free were remains, and via comparing vehicle performance for feasible path, the optimal driving trajectory was generated.

As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

Function and performance test of automated vehicles in the closed field is a necessary way to verify their safety, intelligence and comfort. The design and number of test scenarios will influence if the automated vehicles can be tested and evaluated effectively and fast. Based on the interrelationship among the vehicle, driver’s (or control system) driving strategy and road, we use the permutation and combination method to compare the relative position and movement relations between an automated vehicle (vehicle under test) and the surrounding vehicles to generate a total possible test scenarios group.

Computer modeling and simulation have significantly facilitated the efficiency of product design and development in modern engineering, especially in the automotive industry. For the design and optimization of car models, optimization algorithms usually work better if the initial searching points are within or close to a feasible domain. Therefore, finding a feasible design domain in advance is beneficial. A data mining technique, Iterative Dichotomizer 3 (ID3), is exploited in this paper to identify sets of reduced feasible design domains from the original design space. Within the reduced feasible domains, optimal designs can be efficiently obtained while releasing computational burden in iterations. A mathematical example is used to illustrate the proposed method. Then an industrial application about automotive structural optimization is employed to demonstrate the proposed methodology. The results show the proposed method’s potential in practical engineering.

The design optimization of vehicle body structure is addressed to reduce interior noise and improve customer satisfaction in this paper. The structural-acoustic model is established and the response of sound pressure in frequency domain is obtained by using finite element method. The minimization of sound pressure near the driver's right ear depends on the geometry of vehicle body structure and the layout of damping treatments. The panel participation analysis is performed to find out the key panels as design variables and improve the efficiency of optimization computation. Response Surface Method (RSM) is utilized to optimize the vibro-acoustic properties of vehicle body structure instead of complex structural-acoustic coupling finite element model. Geometric optimization problem of panels is described and solved to minimize the interior noise in vehicle.