This seminar is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). As the complexity of products increases, traditional text-based systems engineering can no longer meet the needs. To solve the problem, Model-based Systems Engineering offers a unified communication platform among relevant staff by carrying out diagram-based unambiguous description, analysis and design for the demand, structure and behavior of complex systems in the form of a model.
In this paper, the distributed parameter method is used to establish the dynamic simulation model of the electric vehicle thermal management system and various parts, and the finite difference method is used to solve the calculation. A thermal management system model for electric vehicles is established by AMESIM to verify the accuracy of the model established in this paper. The model established in this paper is compared with the change trend of refrigerant temperature, pressure and flow rate at the outlet of each component of the system calculated based on the model established by AMESIM, which verifies the correctness of the model established in this paper. Using the established model, the influence of the refrigerant flow on the cooling performance of the battery pack and the influence on the heating comfort of the passenger compartment were studied, and a control strategy for the rapid cooling of the battery pack was proposed.
Motor thermal management of electric vehicles (EVs) is becoming more significant due to its close relations to vehicle aerodynamic performance and energy consumption, while computer aided engineering (CAE) plays an important role in its development. A 1D-3D coupled model is established to characterize transient thermal performance of the motor in an electric vehicle on a high performance computer (HPC) platform. The 1D motor thermal management model is integrated with the 1D powertrain model, and a 3D thermal model is established for the motor, while online data exchange is realized between the 1D and 3D models. The 1D model gives boundaries such as inlet coolant temperature, mass flowrate and motor heat generation to the 3D model, while 3D gives back boundaries such as heat transfer to coolant simultaneously. Transient simulations are performed for the 140kph(20℃) driving cycle, and the model is calibrated with experimental data.
Recently, automobile manufacturers are interested in the development of battery electric vehicle (BEV) having a longer mileage to satisfy customer needs. The BEV with high efficiency depends on the temperature of the electric components. Hence it is important to study the effect of the cooling system in electric vehicle in order to optimize efficiency and performance. In this study, we present a 1-D vehicle thermal management (VTM) simulation model. The individual vehicle subsystems were modeled including cooling, power electric (PE), mechanical, and control components. Each component was integrated into a single VTM model and it would be used to calculate energy transfer among electrical, thermal, and mechanical energy. As a result, this simulation model predicts a plenty of information including the state of each component such as temperature, energy consumption, and operating point about electric vehicle depending on driving cycles and environmental conditions.
Nowadays simulation of the fatigue life is an essential part of the development of components in the automotive and machinery industry. Weak points can be identified fast and reliable with respect to stiffness, strength and lightweight. A pure virtual optimization of the design can be performed without the need of prototypes. Only for the production release a final test is necessary. A lot of parameters influence the fatigue life as the local stress, material, surface roughness, size of the component, temperature etc. Notches have the strongest impact on fatigue life, depending on radius and shape. Stresses at the notch base are increased because the load flow is forced through a reduced cross section, or changes its direction around an inwardly curved edge. But notches cause not only an increase of the local stress. Also, the local fatigue strength is increased because of a support effect from the neighboring areas, where the stress is already reduced.
The CAE industry always moves towards new ways to improve the productivity, efficiency and to reduce the solution times. Conventional method of Cohesive Zone Modelling has drawback of higher computation and modelling time. Due to this problem, sometimes Engineers need to avoid simulations and rely only on some sort of approximation of crack from previous designs. This approximation can lead to either product failure or overdesign of the product. A new approach is discussed in this paper to simulate crack initiation and propagation with Cohesive Zone Modelling. Conventional method uses Cohesive zone modelling with Hex or Penta elements by assigning material with cohesive properties, which increases computation and modelling time. The new approach models Cohesive zone as contact between two bodies, thus eliminating the need to use cohesive elements which will essentially reduce the computation time as well as modelling time.
Uniaxial fatigue tests of rubber dumbbell specimens under different mean and amplitude of strain are carried out. An Extreme Learning Machine (ELM) model optimized by Dragonfly Algorithm (DA) is proposed to predict the fatigue life of rubber based on measured rubber fatigue life data. Mean and amplitude of strain and measured rubber fatigue life are taken as input variables and output variables respectively in DA-ELM model. For comparison, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize ELM parameters, and GA-ELM and PSO-ELM models are established. The comparison results show that DA-ELM model performs better in predicting the fatigue life of rubber with least dispersion. The coefficients of determination for the training set and test set are 99.47% and 99.12%, respectively. In addition, a life prediction model equivalent strain amplitude as damage parameter is introduced to further highlight the superiority of DA-ELM model.
Durability engineering for vehicles is about relating real operational loading to the actual strength of the product and its components. In the first part of this presentation, we show how to calculate failure probabilities and safety factors based on the load and strength distributions. We discuss the uncertainty within the estimations, which is considerably large in case of extremely small failure probabilities as required for safety critical components. In the second part, we focus on modelling and simulating the loads based on real vehicle usage, such that the resulting statistics allows to understand and quantify the usage variability. The idea is, to simulate thousands of vehicle life spans of, say, 300.000 km or 15.000 h of operation each. The input data for such simulations typically consists of a combination of geographic data (like road network, topography, road conditions, traffic data, and points of interest) and properly segmented rich data from measurement campaigns.
Design of HVAC system plays an important role in acoustic comfort for passengers. With automotive world moving towards electrical vehicles where powertrain noise is low, designing low noise HVAC system is becoming more important. For an automobile manufacturer, ability to predict the production vehicle cabin noise at the early design stage is important as it allows more freedom for design changes, which can be incorporated in the vehicle at lower cost. Although HVAC prototype and system level testing at early design stage is possible for noise estimation but flow field is not visible in test that makes difficult to improve design. CFD simulation can provide detailed information on flow field, noise source strength and location. But in such a simulation, accurate prediction has been a challenge due to the inability of CFD tools to model acoustic absorptive characteristics of interior walls of cabin.
As one of the key components of the heat pump system, the electronic expansion valve mainly plays the role of throttling and reducing pressure in the heat pump system. The refrigerant flowing through the orifice will produce complex phase change. It is of great significance to study the internal flow field by means of CFD calculations. Firstly, a three-dimensional fluid model is established and the mesh is divided. Secondly, the phase change model is selected, the material is defined and the boundary conditions are determined. According to the principle of the fluid passing through thin-walled small holes, the flow characteristics of electronic expansion valve are theoretically analyzed. Then the flow characteristics of expansion valve are numerically calculated, and a bench for testing mass flow rate of the expansion valve is built. Then the theoretical value, CFD value and experimental value are compared to verify the correctness of the established three-dimensional fluid model.
High strength and thin materials are widely adopted in modern electric vehicles for lightweight design to achieve high energy efficiency. For battery modules, 5000 and 6000 aluminum are typically utilized as a structural material with a thickness range between 1 to 5 mm. Laser welding is one of the most optimum welding tools for joining such a thin material due to its unique advantages, e.g., high welding speed, high accuracy, high energy yet the smallest possible heat affect zone, etc. This paper aims to develop a simplified yet effective FE modeling procedure to simulate the laser welding effects on the aluminum structures used in electric vehicle battery modules. A sequentially-coupled thermo-mechanical analysis procedure is developed to determine the softened zone size for aluminum weldments. Then a tie-rupture weld model incorporates the softened zone to predict the weld failure strength.
The parameter setting has a great influence on the noise reduction performance of the road noise active control (RNC) system. This paper analyzes and optimizes the parameters of the RNC system. Firstly, the model of the RNC system is established based on the FxLMS algorithm. Based on this model, taking the maximum noise reduction as the evaluation index, the sensitivity analysis of convergence coefficient, filter order, and reference signal gain was carried out using the Sobol method with the data measured by a real vehicle on asphalt pavement at 40km/h. The results show that there is no significant interaction between the three parameters. Then, using the idea of orthogonal experiment, the simulation results of the control model are analyzed by taking the maximum noise reduction as the evaluation index. It is found that the convergence coefficient has the greatest effect on the maximum noise reduction, followed by the filter order, and the reference signal gain has the least effect.
High-speed vehicle is prone to instability under bad road conditions, causing many safety accidents such as tail-flicking and overturning. Stability control could assist vehicle to drive safely and stably by adjusting the additional yaw moment. However, most of the existing stability control strategies directly invoke the information of the sideslip angle of the centroid that is difficult to obtain on the vehicle, and carry out complex controller design, which deviates from the actual application. In order to achieve a complete set of stability control architecture oriented to practical applications, this paper designs a hierarchical vehicle stability control strategy based on differential braking and state estimation technology.
Decreasing fuel consumption in Internal Combustion Engines (ICE) is a key target for engine developers in order to achieve the CO2 emissions limits during a standard cycle. In this context, reduction of engine friction can help meet those targets. The use of Low Viscosity Engine Oils (LVEOs), which is currently one of the avenues to achieve such reductions, is studied in this manuscript through a validated numerical simulation model that predicts the friction of the engine’s piston-cylinder unit, journal bearings and camshaft. These frictional power losses are obtained for four different lubricant formulations which differ in their viscosity grades and design. Results show a maximum friction savings of up to 6% depending on the engine operating condition, where the major reductions come from hydrodynamic-dominated components such as journal bearings, despite an increase in friction in boundary-dominated components such as the piston-ring assembly.
The boundary load of the light truck cab fatigue analysis is the force of the cab mounting system, which cannot be directly measured. In the absence of a physical prototype, the fatigue load of the cab cannot be extracted through virtual iteration. Aiming at the problem of fatigue analysis in the early stage of the car-free cab, the virtual proving ground technology is used to extract fatigue load and do fatigue analysis in this paper. Using the virtual road as the excitation, the simulation analysis of the whole vehicle virtual proving ground is carried out, and the wheel center load and the cab mounting force are obtained. Comparing the simulation load with the signal required on the proving ground, it is found that the extracted virtual load is consistent with the actual vehicle load in the time domain and frequency domain. The retention of pseudo-damage of the six-component load of the wheel center can meet the precision control requirements of the vehicle load decomposition.
This paper presents the design procedure of a vehicle battery pack, in terms of electrical and mechanical requirements with an innovative methodology to model Li-ion cells’ thermo-electro-mechanical behaviour. This modelling approach can predict, through FEM analysis, if short circuit happen with consequent generation of fire in case of vehicle crash. This last aspect has several issues related to the multiphysics characteristics of the phenomena due to the fact that battery cells are made up by really thin components and, as consequence, not significant works of an entire deformable battery pack simulation have been found in literature. For this reason, the design approach studied overcomes the classical methodology in which cells’ mechanical behaviour is considered unknown to understand if cell failure appears avoiding over-engineered battery pack structure. At the beginning, a benchmarking activity on existing FEM modelling methodologies of single cells has been conducted.
The fuel spray process is of utmost importance to internal combustion engine design as it determines engine performance and emissions characteristics. While designers rely on CFD for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. Utilizing these detailed experimental results, we have explored a machine learning approach to prediction of fuel sprays. The ML approach for spray prediction is promising because (1) it does not require phenomenological spray models, (2) it can provide time-resolved spray data without time-stepping simulation, and (3) it is computationally faster than CFD. In this study, a pixel-regression model has been developed and applied for gasoline spray prediction.