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There are a large number of uncertainties in engineering design, and the accumulated uncertainties will enlarge the overall failure probability of the structure system. Therefore, structural design considering uncertainties has good guiding significance for improving the reliability of engineering structures. To address this issue, the dynamic-static structural topology optimization is established and reliability-based topology optimization with decoupling format is conducted in this study. The design point which satisfying the constraint of the target reliability indicator is obtained according to the reliability indicators of the first-order reliability method, and the uncertain design variables are modified into a deterministic variable according to the sensitivity information.

The action of load on the component is crucial to evaluate the performance of durability. Another factor that affects fatigue life is the boundary conditions of the test specimen being tested by introducing unrealistic loads on the component of interest. The physical test is widely conducted in the laboratory. The fixture provides additional constraints on the test specimen as well as reaction forces to balance the test system [1]. The characteristics of the fixture involved in the test is important to analyze and assess the test results [2]. The impact of the reaction force of the fixture on the spindle-coupled axle road simulation test is presented in this article. A simplified 7-DoF (degrees of freedom) model is introduced to demonstrate the dynamic behavior of the vehicle. The influence on the internal load by the fixture has been analyzed. Followed by a more detailed MBS (multibodysystem) model to give a thorough understanding of the phenomenon.

Based on self-designed battery management system, the hardware construction and software strategy is researched for decreasing system's power consumption. Moreover, four different working modes are set to control the system. They are normal mode, idle mode, standby mode and sleep mode, among which the system can switch according to definite internal or external conditions so as to realize as low power consumption as possible. Especially, when vehicle stops for a long time, the system enters the sleep mode through controlling hardware and software, where extremely low power consumption is achieved. The strategy of low power consumption also has its general value for other vehicle embedded systems.

As is known to all, the structure of the chassis has been greatly simplified as the application of in-wheel motor in electric vehicle (EV) and distributed control is allowed. The micro EV can alleviate traffic jams, reduce the demand for motor and battery capacity due to its small size and light weight and accordingly solve the problem that in-wheel motor is limited by inner space of the wheel hub. As a result, this type of micro EV is easier to be recognized by the market. In the micro EV above, two seats are side by side and the battery is placed in the middle of the chassis. Besides, in-wheel motors are mounted on the rear axle and only front axle retains traditional hydraulic braking system. Based on this driving/braking system, distribution of braking torque, system reliability and braking intensity is analyzed in this paper.

For distributed drive electric vehicles (DDEVs), the influence of the power ratio between the front and rear motors on the energy efficiency characteristics is investigated. The power-train systems of the DDEVs in this study are divided into two different power-train configurations. The first is with its front axle driven by wheel-side motors and the rear axle driven by in-wheel motors, and the second is with both the front and rear axles driven by in-wheel motors. The energy consumption simulation and analysis platform of the DDEV is built with Matlab/Simulink. The parameters of the key components are determined by the experiments to ensure the validity of the data used in simulation. At the same time, the vehicle’s average energy efficiency coefficient is defined to describe the energy efficiency characteristics of the power-train strictly. Besides, the control strategies for driving and braking of the DDEV based on energy efficiency optimization are presented.

The electromagnetic valve actuator (EMVA) is considered a technological solution for decoupling between crankshaft and camshaft to improve engine performance, emissions, and fuel efficiency. Conventional EMVA consists of two electromagnets, an armature, and two springs has been proved to have the drawbacks of fixed lift, impact noise, complex control method and large power consumption. This paper proposes a new type of EMVA that uses voice coil motor (VCM) as electromagnetic valve actuator. This new camless valvetrain (VEMA) is characterized by simple structure, flexible controllable and low actuating power. VCM provides an almost flat force versus stroke curve that is very useful for high precision trajectory control to achieve soft landing within simple control algorithm.

Traditional active suspension which is equipped with hydraulic actuator or pneumatic actuator features slow response and high power consumption. However, electromagnetic actuated active suspension benefits quick response and energy harvesting from vibration at the same time. To design a novel active and energy regenerative suspension (AERS) utilizing electromagnetic actuator, this paper investigates the benchmark cars available on the market and summaries the suspension features. Basing on the investigation, a design reference for AERS design is proposed. To determine the parameters of the actuator, a principle is proposed and the parameters of the actuator are designed accordingly. Compared the linear type and rotary type Permanent Magnet Synchronous Motor (PMSM), the rotary type is selected to construct the actuator of the AERS. Basing on the suspension structure of the design reference model and utilizing rotary type PMSM, a novel AERS structure is proposed.

This paper constructs the Accident Crash Scenarios(ACSs) classification system based on the traffic accident data collected by the traffic management department in a Chinses city from 2013 to 2015. The classification system selects four influenced variables on the basis of Critical Driving Scenarios(CDSs) in Naturalistic Driving Data. The proportions of each variable are analyzed, and all ACSs are divided into 48 scenarios. The highest proportion of nine ACSs are extracted from all 10596 ACSs, and the result shows the ACSs involved Car-Pedalcyclist occupy the top four scenarios, and the scenarios involved intersection situations are worth attention. Pedalcyclists include bicyclists, motorcyclists, tri-cyclists and electric bicyclists. Multivariate Logistic Regression(MLR) analysis is then used to study the ACSs involved the type of Car-Pedalcyclist.

Fatigue defect and failure of rubber element widely used in mechanical systems could seriously affect the safety and reliability of systems in practical operation. Because rubber element is considered as hyperelastic material, traditional σ - N curve which is usually used in metal material for fatigue life analysis can not be used here. The fatigue life of rubber bushing in automobile engine cradle was analyzed by using the energy method. The Yeoh model coefficients were given by tensile test of natural rubber, and the estimating formula for fatigue life of natural rubber was obtained by finite element calculation and fatigue test. Maximum strain energy density was treated as the parameter of fatigue damage, then the rubber bushing fatigue life was calculated by the estimating formula. The results were verified by test of rubber bushing, which indicated that the model mentioned in this paper is accuracy enough.

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.

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve).

Hydrous ethanol not only has the advantages of high-octane number and valuable oxygen content, but also reduce the energy consumption in the production process. However, little literature investigated the internal flow and cavitation of hydrous ethanol-gasoline fuels in the multi-hole direct injector. In this simulation, a two-phase fuel flow model in injector is established based on the multi-fluid model of Euler-Euler method, and the accuracy of model is verified. On the basis of this model, the flow of different hydrous ethanol-gasoline blends is calculated under different injection conditions, and the cavitation, flow rate, and velocity at the outlet of the nozzle are predicted. Meanwhile, the influence of temperature and back pressure on the flow is also analyzed. The results show that the use of hydrous ethanol reduces the flow rate, compared with the velocity of E0, that of E10w, E20w, E50w, E85w, and E100w decreases by 10%, 12.9%, 17.6%, 20%, and 23.5%, respectively.

Proton exchange membrane fuel cell (PEMFC) system is considered as one of the most popular power sources because of its high energy density, fast dynamic response and zero pollution. However, the start-up at low temperature (e.g. - 30 °C) is still a major challenge for its wide application due to water freezing in Membrane Electrode Assembly (MEA). In this paper, a cold start test process in an environment cabin with auxiliary heat was carried out for a full power automotive PEMFC system, including normal operation, shutdown purge and cold start processes analysis from -30°C. Rated power of this stack is 100kW at the current density of 1.4A/cm2 and relevant maximum output power can reach to 120kW. In order to reduce the damage of high potential to MEA, on-load purge with a current of 30A is conducted to removing extra water in stack for improving cold start ability. Based on corresponding control strategy, cold start was realized successfully within 110s.

To improve the maneuverability and energy consumption of an electrical vehicle, a two-level speed control method based on model predictive control (MPC) is proposed for accurate control of the vehicle during downhill coasting. The targeted acceleration is planned using the anti-interference speed filter and MPC algorithm in the upper-level controller and executed using the integrated algorithm with the inverse vehicle dynamics and proportional-integral-derivative control model (PID) in the lower-level controller, improving the algorithm’s anti-interference performance and road adaptability. Simulations and vehicle road tests showed that the proposed method could realize accurate real-time speed control of the vehicle during downhill coasting. It can also achieve a smaller derivation between the actual and targeted speeds, as well as more stable speeds when the road resistance changes abruptly, compared with the conventional PID method.

Harmony-with-traffic refers to the ability of autonomous vehicles to maximize the driving benefits such as comfort, efficiency, and energy consumption of themselves and the surrounding traffic during interactive driving under traffic rules. In the test of harmony-with-traffic, one or more background vehicles that can respond to the driving behavior of the vehicle under test are required. For this purpose, the functional requirements of car-following model for harmony-with-traffic evaluation are analyzed from the dimensions of test conditions, constraints, steady state and dynamic response. Based on them, an interactive car-following model (ICFM) is developed. In this model, the concept of equivalent distance is proposed to transfer lateral influence to longitudinal. The calculation methods of expected speed are designed according to the different car-following modes divided by interaction object, reaction distance and equivalent distance.

Autonomous driving related technologies have become a hot topic in academia and industry. Planning control is one of the core technologies of autonomous driving, which is conducive to vehicles safe and efficient driving. This paper proposes a novel optimal speed control algorithm, which considers the power system's energy consumption, the speed limit on the road, and the safe distance of the vehicle in front. An optimal speed control model of “From battery to wheel” energy consumption is established by constructing a performance index function based on the best-fitting formula of motor power, motor speed and torque. Based on the optimal control principle, the fourth-order ordinary differential equation of the speed control model is established, based on the indirect adjoining approach, the speed control model under the restriction of the road speed limit and safe distance of the preceding vehicle is derived and the analytical expression is obtained.

The foundation of both advanced driving assistance system(ADAS) and automated driving (AD) is an accurate environment perception system(EPS). However, evaluation and test method of EPS are seldom studied. In this paper, naturalistic driving environment was studied and test scenarios for EPS under rear-end collision risk were proposed accordingly. To describe driving environment, a new concept named environment perception element(EPE) was first proposed in this paper, which refers to all the objects that the EPS must perceive during driving. Typical environment perception elements include weather and light conditions, road features, road markings, traffic signs, traffic lights, other vehicles, pedal cyclists and pedestrians and others. Driving behaviors collected in Shanghai, China were classified and rear-end collision risk scenarios were obtained and described using EPEs. Probability distribution of EPEs was therefore obtained.

On account of environmental friendliness, high energy conversion efficiency and high power density, the proton exchange membrane fuel cell (PEMFC) has been used for automotive application for years. However, its durability in powertrain system is one of technical challenges, which restricts the large-scale commercialization of fuel cell electric vehicles (FCEV). In addition to the complex aging mechanism of PEMFC, the durability and energy relationship of key components in powertrain system, including battery and DC/DC converter, have a crucial impact on the vehicle performance, which have not been thoroughly analyzed. Nowadays, most researchers have explored the causes of components degradation from models or experiments and tried to carry out the life expectancy. Nevertheless, it is in need of system-level researches on durability against the actual automotive application.

In order to satisfy design requirements of Lane Keeping Assistance System (LKAS), a Driver Steering Override (DSO) strategy is necessary for driver’s interaction with the assistance system. The assistance system can be overridden by the strategy in case of lane change, obstacle avoidance and other emergency situations. However, evaluation and optimization of the DSO strategy for LKAS cannot easily be completed quantitatively considering driver’s acceptability. In this research, firstly subjective and objective evaluation experiment is designed. Secondly, correlations between the subjective and the objective evaluation results are established by using regression analysis. Finally, based on the correlations established previously, the optimal performance of DSO strategy is obtained by setting the desired comprehensive evaluation ratings as the optimized goal.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.