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Crashworthiness is one of the most important performances of vehicles, and the front rails are the main crash energy absorption parts during the frontal crashing process. In this paper, the front rail was simplified to a thin-walled beam with a cross section of single-hat which was made of steel and aluminum. And the two boards of it were connected by riveting without rivets. In order to optimize its crashworthiness, the thickness (t), radius (R) and the rivet spacing (d) were selected as three design variables, and its specific energy absorption was the objective while the average impact force was the constraint. Considering the error of manufacturing and measurements, the parameters σs and Et of the steel were selected as the uncertainty variables to improve the design reliability. The algorithm IP-GA and the approximate model-RBF (Radial Basis Function) were applied in this nonlinear uncertainty optimization.

It is becoming an increasingly important issue to improve NVH performance of electric drive motor for electric vehicle as the market grows rapidly. The correlation between stiffness of rear cover of motor, rotor eccentricity and noise of an electric drive is discussed in this paper which was few mentioned before. Poor stiffness of bearing chamber of rear cover may cause rotor eccentricity, which would lead to additional orders of electromagnetic noise. Stiffness optimization model of rear cover of motor was established, and the Optistruct of Hyper works software was used to improve stiffness as well as mode frequency by designing circular and radial ribs to surround bearing chamber of rear cover under guidance of topology. As compared to basis model with same mass, the 1st and 2nd strict mode frequencies of optimized rear cover separately increased by 11% and 12.5% with numerical simulations.

Carbon fiber reinforced plastic (CFRP) composite materials have gained particular interests due to their high specific modulus, high strength, lightweight and perfect corrosion resistance. However, in reality, CFRP composite materials cannot be used alone in some critical places such as positions of joints with hinges, locks. Therefore, metal reinforcements are usually necessary in local positions to prevent structure damage. Besides, if uncertainties present, obtained optimal structures may experience in failures as the optimization usually pushes solutions to the boundaries of constraints and has no room for tolerance and uncertainties, so robust optimization should be considered to accommodate the uncertainties in practice. This paper proposes a mixed topology method to optimize metal and carbon fiber reinforced plastic composite materials simultaneously under nondeterministic load with random magnitude and direction.

X-in-the-loop (XiL) framework is a new validation concept for vehicle product development, which integrates different virtual and physical components to improve the development efficiency. With XiL platform the requirements of reproducible test, optimization and validation, in which hardware, equipment and test objects are located in different places, could be realized. In the view of different location and communication form of hardware, equipment and test objects, time delay problem exists in the XiL platform, which could have a negative impact on development and validation process. In this paper, a simulation system of time delay prediction and compensation is founded with the help of BP neural network and RBF neural network. With this simulation system the effect of time delay in a vehicle dynamic model as well as tests of geographically distributed vehicle powertrain system is improved during the validation process.

In this paper, a case study of Shanghai HEVs application and its effects on the social and environmental benefits are presented based on the multi views on the different aspects, such as, not only for the fuel consumption saving, but also emissions reduction and health effect, agriculture loss and cleaning cost. The results show that the potential benefits for the society from HEVs application are markedly with the increase of the ratio of HEV in the population of vehicle. Based on this, the policy to promote the HEV purchased by consumers is very important at the beginning of HEV into market.

There are often a large number of design variables and responses in suspension hard point optimization design. The traditional optimization strategy integrating heuristic algorithm and simulation model is not applicable due to its low efficiency. To solve optimization problems with huge number of design variables and responses, a multi-objective optimization framework combined heuristic optimization algorithm with multi-objective decision-making method is developed. Specifically, the multi-objective optimization was performed by dividing the problem into two independent sub-problems of multi-objective optimization and multi-objective decision-making. Further, to reduce the number of sample points required for building a surrogate model, a two-stage multi-objective optimization is proposed.

The nonuniformity property of the temperature field distribution will not only affect on the battery charging and discharging performance but also its lifetime. In this paper the elementary structural design is implemented for Ni-Mh battery package and the corresponding test platform is constructed from the point of view of temperature difference control strategy, the test results show that the present structural design schemes can effectively restrain temperature difference enlargement among the battery stacks. Through the application of adopting the flow field uniformity method to control temperature difference, and flow field optimization inside the battery package, it is found that the flow field velocity change quantity ΔV is gradually reduced as the increase of the afflux hood angle Ak and air vent width Da, and the difference of battery temperature is relatively lower, which denoting that the corresponding relationship can be created based on test data.

The Automatic Parking System (APS) is consisted of environmental perception, path planning and path following. As one of the key technologies in APS, path following module controls the lateral movement of the vehicle during the parking process. A mature path following module should meet all the performance indexes of high precision, fast convergence, convenient tuning and good passenger comfort. However, the current path following control methods can only meet parts of the performance indexes, instead of all. In order to satisfy all the performance indexes above, a path following control method based on Linear Quadratic Regulator (LQR) is proposed in this paper. Firstly, the linearization of the non-linear vehicle kinematic model was done to establish a linear system of the path following error. Secondly, LQR optimal control was used to achieve the closed-loop control of this linear system to guarantee its stability and fast convergence property.

The price of fossil fuels and the increasing inexorable energy crisis have become vital issues for everyone. Tongji University EconoPower Racing Team was established to participate in the “Honda EconoPower Cup” annually. Every contestant in the competition must finish a certain distance in the fixed time, with the gasoline supplied by the committee. After that the committee will measure the fuel consumption of every team and calculate the distance per liter fuel (the farther the better) to determine the champion. In order to enhance the EP vehicle's achievement we've made some improvements, such as framework, body, engine's optimization and so on. In this passage we mainly state some details of our research approaches in framework, steering, transmission, shape and driving strategy. The main technologies were: friction reduction, lightweight, enhancement of power train efficiency, tire selection and driving strategy.

The present work discusses a novel oxy-fuel combustion cycle utilized in compression ignition internal combustion engine. The most prominent feature of this cycle is that the air intake is replaced by oxygen; therefore nitric oxide (NOX) emission is eliminated. The enrichment of oxygen leads to higher flame speed and mass fraction consumption rate; on the other hand, the high concentration of oxygen presented during combustion will result in intense pressure rise rate which may cause severe damage to engine hardware. As water injection is already utilized in gasoline engine to control knocking, the utilization of water injection in optimizing oxy-fuel combustion process has been tested in this study. To understand the relationship between water injection strategy and cycle efficiency, computational fluid dynamics (CFD) simulations were carried out. The model was carefully calibrated with the experimental results; the errors were controlled within 3%.

The separation and analysis of the torque of the permanent magnet synchronous motor is of great significance for optimizing the torque output of the motor. Based on the frozen permeability method, the virtual work principle (VWP) or the Maxwell stress tensor method (MSTM) is often used to separate the torque for torque analysis. However, considering the influence of non-ideal factors such as motor saturation, cross-coupling and flux harmonics, there are differences in torque separation between the VWP and the MSTM, which has been researched and analyzed in this paper. Based on this, for the assisted airspace barrier design of a surface-inserted permanent magnet synchronous motor, to conduct theoretical research on the torque optimization design, this paper uses the VWP to separate the average torque and the MSTM to separate the torque ripple.

As the electric vehicle market grows rapidly, thermal analysis related to the performance of electric drive motors has gained increasing interest. However, it is hard to obtain rotor temperature for torque correction during operation which leads to unexpected inaccurate control of motors. Rotor temperature monitoring and torque correction for IPMSM (Interior Permanent Magnet Synchronous motor) of new Energy vehicles are discussed in this paper. Considering the practical application, a low-order lumped parameter thermal network (LPTN) composed of three nodes is built for calculating the rotor temperature under different operating conditions on a 160kw IPMSM of a three-in-one electric drive. To identify the parameters of LPTN, the measurements were done on a test bench with a prototype of the three-in-one electric drive. K-type thermocouples were used to directly measure the temperature of each node.

The mechanical claw is an important functional part of intelligent sanitation vehicles. Its performance significantly influences the functional reliability and structural safety of intelligent sanitation vehicles. The load of the trash changes extensively during the work of the mechanical claw. Hence, a comprehensive consideration of structural uncertainty during designing is needed to meet performance requirements. Uncertainty optimization design should be applied to reduce the sensitivity of structural performance to uncertain factors and ensure the robust performance of the mechanical paw structure. In this study, a numerical model of the mechanical claw of novel intelligent sanitation vehicles is established first in SolidWorks, and a finite element model is built by Optistruct. Based on the analysis of uncertain load factors of the mechanical claw, a robust mathematical model of uncertain factors is established by the Gauss-Chebyshev and Smolyak algorithm.

The conducted emissions of DC motors have been a very important content when testing electromagnetic noise. It has certain practical guiding significance to optimize and improve the motor in the design stage through the simulation of conducted emissions circuit level. The existing literature research shows that the simulation results can not reflect the test results of the conducted emissions of the motor well because it is difficult to accurately model the armature winding and the commutation process of the motor in the electromagnetic simulation of the conducted emissions circuit level of the DC brush motor. In this paper, an electromagnetic simulation modeling method for conducted emissions of DC motor is proposed. The circuit model established in the PSPICE can simulate the actual conducted emissions values of DC motor by the method.

The driver monitoring system (DMS) plays an essential role in reducing traffic accidents caused by human errors due to driver distraction and fatigue. The vision-based DMS has been the most widely used because of its advantages of non-contact and high recognition accuracy. However, the traditional RGB camera-based DMS has poor recognition accuracy under complex lighting conditions, while the IR-based DMS has a high cost. In order to improve the recognition accuracy of conventional RGB camera-based DMS under complicated illumination conditions, this paper proposes a lightweight low-illumination image enhancement network inspired by the Retinex theory. The lightweight aspect of the network structure is realized by introducing a pixel-wise adjustment function. In addition, the optimization bottleneck problem is solved by introducing the shortcut mechanism.

In this paper, an equivalent conversion method is proposed to apply the six-dimensional force road spectrum of the four-axle vehicle on the same platform to the three-axle through the axle load comparison. Further, the feasibility of the devolved equivalent conversion method is verified, and the fatigue performance improvement of the wishbone support structure of a commercial vehicle is finally achieved. Specifically, firstly, the load spectrum at each attachment point of the suspension for the three-axle vehicle is obtained through the iteration of the multi-body dynamic model. Furthermore, the finite element model of the suspension for the three-axle vehicle is established; the analysis of fatigue life for the suspension structure is performed by extracting stress amplitude through the multi-axis cyclic counting method and calculating equivalent force amplitude through McDiarmid’s criterion, combined with the SN curve of the material.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

Lithium-ion batteries have been applied in the new energy vehicles more and more widely. The inconsistency of battery cells imposes a lot of difficulties in parameter and state estimations. This paper proposes a new algorithm which can online identify the parameters of each individual battery cell accurately with limited increase of computational cost. An equivalent circuit battery model is founded and based on the RLS (recursive least squares) algorithm, an optimization algorithm with the construction of weight vectors is proposed which can identify the parameters of lithium battery pack considering inconsistency of single battery cell. Firstly, the average value of the parameters of the battery pack is identified with the traditional RLS algorithm. Then the ratios between the parameters of each battery cell can be deduced from the mathematical model of battery. These ratios are used to determine the weight vector of each parameter of individual battery cells.