Search Results

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Active steering systems can help the driver to master critical driving situations. This paper presents a fuzzy logic control strategy on active steering vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model using ADAMS can accurately predict the dynamic performance of the vehicle. A new hybrid steering scheme including both active front steering (applying an additional front steering angle besides the driver input) and rear steering is presented to control both yaw velocity and sideslip angle. A set of fuzzy logic rules is designed for the active steering controller, and the fuzzy controller can adjust both sideslip angle and yaw velocity through the co-simulation between ADAMS and the Matlab fuzzy control unit with the optimized membership function. To ensure the design of high-quality fuzzy control rules, a rule optimization strategy is introduced.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Interval inverse problems can be defined as problems to estimate input through given output, where the input and output are interval numbers. Many problems in engineering can be formulated as inverse problems like vehicle suspension design. Interval metrics, instead of deterministic metrics, are used for the suspension design of a vehicle vibration model with five degrees of freedom. The vibration properties of a vehicle vibration model are described by reasonable intervals and the suspension interval parameters are to be solved. A new interval inverse analysis method, which is a combination of Chebyshev inclusion function and optimization algorithm such as multi-island genetic algorithm, is presented and used for the suspension design of a vehicle vibration model with six conflicting objective functions. The interval design of suspension using such an interval inverse analysis method is shown and validated, and some useful conclusions are reached.

As there are a variety of uncertainty contained in dynamic systems, this paper presents a method to identify the uncertain parameters of Load Sensing Proportional Valve in a heavy truck brake system. This method is derived from polynomial chaos theory and uses the maximum likelihood approach to estimate the most likely value of uncertain parameters, such as equivalent bearing area diameter of the diaphragm, preload of return spring and so on. The maximum likelihood estimates are obtained through minimizing the cost function derived from the prior probability for the measurement noise. Direct stochastic collocation has been shown to be more efficient than Galerkin approach in the simulation of systems with large number of uncertain parameters. The simulation model of Load Sensing Proportional Valve is built in software AMESim based on logic structure of the valve. The uncertain parameters are estimated through the simulation results which are treated as measurements.

In this paper, a new computational method is provided to identify the uncertain parameters of Load Sensing Proportional Valve (LSPV) in a heavy truck brake system by using the polynomial chaos theory. The simulation model of LSPV is built in the software AMESim depending on structure of the valve, and the estimation process is implemented relying on the experimental measurements by pneumatic bench test. With the polynomial chaos expansion carried out by collocation method, the output observation function of the nonlinear pneumatic model can be transformed into a linear and time-invariant form, and the general recursive functions based on Newton method can therefore be reformulated to fit for the computer programming and calculation. To improve the estimation accuracy, the Newton method is modified with reference to Simulated Annealing algorithm by introducing the Metropolis Principle to control the fluctuation during the estimation process and escape from the local minima.

For vehicles equipped with dry dual clutch transmission, due to the diversity of starting conditions, it is a nontrivial task for control strategy to meet the requirements of all kinds of complex starting conditions, which is easy to cause large starting shock and serious clutch wear. Therefore, it is proposed in this paper an adaptive control strategy for complex starting conditions by adjusting two clutches to participate in the starting process at the same time. On the basis of establishing the transmission system model and clutch model, the starting conditions are identified in terms of starting speed, road adhesion and driver's intention, in which the driver's intention is identified by fuzzy reasoning model. Based on the identification of starting conditions and considering the safety principle, it is selected the appropriate starting gear and clutch combination mode, and adjusted the combination speed of the two clutches to carry out an adaptive control strategy.

Intelligent vehicle-to-everything connectivity is an important development trend in the automotive industry. Among various active safety systems, Autonomous Emergency Braking (AEB) has garnered widespread attention due to its outstanding performance in reducing traffic accidents. AEB effectively avoids or mitigates vehicle collisions through automatic braking, making it a crucial technology in autonomous driving. However, the majority of current AEB safety models exhibit limitations in braking modes and fail to fully consider the overall vehicle stability during braking. To address these issues, this paper proposes an improved AEB control system based on a risk factor (AERF). The upper-level controller introduces the risk factor (RF) and proposes a multi-stage warning/braking control strategy based on preceding vehicle dynamic characteristics, while also calculating the desired acceleration.

Nowadays, studying the human body response in a seated position has attracted a lot of attention as environmental vibrations are transferred to the human body through floor and seat. This research has constructed a multi-body biodynamic human model with 17 degrees of freedom (DOF), including the backrest support and the interaction between feet and ground. Three types of human biodynamic models are taken into consideration: the first model doesn't include the interaction between the feet and floor, the second considers the feet and floor interaction by using a high stiffness spring, the third one includes the interaction by using a soft spring. Based on the whole vehicle model, the excitation to human body through feet and back can be obtained by ride simulation. The simulation results indicate that the interaction between feet and ground exerts non-negligible effect upon the performance of the whole body vibration by comparing the three cases.

Diesel emission aftertreatment system is usually designed to meet stringent packaging constraints, rendering a difficult situation to achieve perfect flow distribution inside the catalytic unit. The non-uniform flow pattern leads to a mal-distribution of flow velocity, temperature, and gas species in catalyst unit. Some catalysts are exposed to harsh working environment, while the rest catalysts are underutilized. This lowers the efficiency of overall catalyst unit and thus requires an oversized system to meet emission requirements. The flow mal-distribution also accelerates the uneven catalyst degradation, lowering the system durability. Hence, a quantitative description of packaging impact on catalyst performance is critical to assess the system efficiency and durability. In the present work, a mapping method is developed to combine catalyst performance with computational fluid dynamics (CFD) simulation.

A simulation model of the single cone synchronizer is presented using the dynamic implicit algorithm with commercial Finite Element Analysis (FEA) software Abaqus. The meshing components include sleeve gear, blocking ring and clutch gear, which are all considered as deformation body. The processes mainly contain the contact between sleeve teeth and blocking teeth, meshing period and the impact of sleeve teeth and clutch gear teeth, and these nonlinear contact steps are realized with Abaqus. In addition, a shift force derives from experiment is applied to the sleeve ring, and a moment is added to the clutch gear to realize the relative rotational speed. Based on the FEA model, the effect of the varied frictional coefficients between the cone surfaces of blocking ring and clutch gear on the synchronizer time and contact stress is discussed. Variation of stresses and contact force with respect to time are evaluated from this analysis.

Metamodeling approach has been widely used due to the high computational cost of using high-fidelity simulations in engineering design. Interpretation of metamodels for the purpose of design, especially design under uncertainty, becomes important. The computational expenses associated with metamodels and the random errors introduced by sample-based methods require the development of analytical methods, such as those for global sensitivity analysis and uncertainty propagation to facilitate a robust design process. In this work, we develop generalized analytical formulations that can provide efficient as well as accurate global sensitivity analysis and uncertainty propagation for a variety of metamodels. The benefits of our proposed techniques are demonstrated through vehicle related robust design applications.

For achieving viable mass customization of products, product configuration is often performed that requires deep understanding on the impact of product features and feature combinations on customers’ purchasing behaviors. Existing literature has been traditionally focused on analyzing the impact of common customer demographics and engineering attributes with discrete choice modeling approaches. This paper aims to expand discrete choice modeling through the incorporation of optional product features, such as customers’ positive or negative comments and their satisfaction ratings of their purchased products, beyond those commonly used attributes. The paper utilizes vehicle as an example to highlight the range of optional features currently underutilized in existing models. First, data analysis techniques are used to identify areas of particular consumer interest in regards to vehicle selection.

As the world's largest auto producer and consumer, China is both the most promising and complex market given the country's rapid economic growth, huge population, and many regional and segment preference differences. This research is aimed at developing data-driven demand models for customer preference analysis and prediction under a competitive market environment. Regional analysis is first used to understand the impact of geographical factors on customer preference. After a comprehensive data exploration, a customer-level mixed logit model is built to shed light on fast-growing vehicle segments in the Chinese auto market. By combining the data of vehicle purchase, consideration, and past choice, cross-shopping behaviors and brand influence are explicitly modeled in addition to the impact of customer demographics, usage behaviors, and attributes of vehicles.

For automated driving vehicles, path planning and trajectory tracking are the core of achieving obstacle avoidance. Real-time external environment perception and vehicle state monitoring play the important role in the decision-making of vehicle operation. Sensor measuring is an important way to obtain vehicle state parameters, but some parameters cannot be measured due to sensor cost or technical reasons, such as vehicle lateral velocity and side-slip angle. This disadvantage will adversely affect the monitoring of vehicle self-condition and the control of vehicle running, even it will lead to erroneous decision-making of vehicles. Therefore, this paper proposes an automated driving path planning and trajectory tracking control method based on Kalman filter vehicle state observer. Some of vehicle state data can be measured accurately by sensors.

The rubber bushing is the key component to suppress vibration in the suspension system, an accurate constitutive model of rubber bushing should capture the amplitude and frequency dependency. Based on the lumped parameter model, three types of rubber bushing models are applied and compared, including the common Kelvin-Voigt model. To evaluate the model parameter and suitable frequency range, the quasi-static and dynamic tests have been performed. Comparing with the testing result, the fractional Kelvin-Voigt model combined with Berg’s friction has the minimum relative error of dynamic stiffness on the whole. Finally, two examples of chassis bushing under different loading conditions are presented. The rubber force and deflection are analyzed in both the time domain and the frequency domain, and the results show the difference of stiffness and hysteresis loop relative to frequency.

Connected and automated vehicles (CAVs) can improve traffic efficiency and reduce fuel consumption. This paper proposes a cooperative game approach to merging sequence and optimal trajectory planning of CAVs at unsignalized intersections. The trajectory of the vehicles in the control zone is optimized by the Pontryagin minimum principle. The vehicle's travel time, fuel consumption, and passenger comfort are considered to construct the joint cost function, completing the optimal trajectory planning to minimize the joint cost function. Analyzing the different states between neighboring CAVs at the intersection to calculate the minimum safety interval. The cooperative game approach to merging sequence aims to minimize the global cost and the merging sequence of CAVs is dynamically adjusted according to the gaming result. The multi-player games are decomposed into two-player games, to realize the goal of the minimal global cost and improve the calculation efficiency.

Tyre behavior plays an important role in vehicle dynamics simulation. The Magic Formula Tyre Model is a semi-empirical tyre model which describes tyre behavior quite accurately in the handling simulation. The Magic Formula Tyre Model needs a set of parameters to describe the tyre properties; the determination of these parameters is nontrivial task due to its nonlinear nature and the presence of a large number of coefficients. In this paper, the homotopy algorithm is applied to the parameter identification of Magic Formula tyre model. A morphing parameter is introduced to correct the optimization process; as a result, the solution is directed converging to the global optimal solution, avoiding the local convergence. The method uses different continuation methods to globally optimize the parameters, which ensures that the prediction of the Magic Formula model can be very close to the test data at all stages of the optimization process.

To better understand the technical challenges of commercial vehicle electrification, BorgWarner converted a production Internal Combustion Engine (ICE) medium duty truck into a fully electrified vehicle. The resulting vehicle includes a newly developed dual-motor rear Beam eAxle driven by a pair of high-performance silicon carbide (SiC) inverters, an 800V battery system, and a new thermal management system customized for the electric vehicle. This paper will detail the conversion process along with the key components involved in the build. The resulting performance of the fully electrified commercial vehicle will be presented in comparison to the original production vehicle. The primary aim is to outline what is entailed in an electric vehicle conversion and to share the learnings gained throughout this build and development process.

In a divided-exhaust turbocharging system, 1 exhaust valve and port from each cylinder can be directed to the turbocharger turbine (referred to as the Blowdown Path) and the other can bypass the turbocharger (referred to as the Scavenge Path). The Blowdown and Scavenge valve events are determined based on the functions of the blowdown and displacement phases of the exhaust process. In our previous publications of another version of Divided Exhaust Period Turbocharging, the Valve Event Modulated Boost system (VEMB), we demonstrated significant engine efficiency and performance improvements over the base turbocharged engine. Reductions in pumping work and high-load Residual Gas Fraction are the primary reasons for efficiency and performance improvements.