Search Results

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

The purpose of this study is to propose braking characteristics that are easy for drivers to handle in a system in which braking and driving operations are performed by hand. Genetic algorithm optimization of braking characteristics showed that the best deceleration tracking was achieved by an FG diagram with a logarithmic function shape. In contrast, the slope of the optimal FG diagram tended to decrease as the driver's proportional gain increased.

In recent years, swift changes in market demands toward achieving carbon neutrality have driven significant developments within the automotive industry. Consequently, employing computer simulations in the early stages of vehicle development has become imperative for a comprehensive understanding of performance characteristics. Of particular importance is the cooling performance of vehicles, which plays a vital role in ensuring safety and overall performance. It is crucial to predict optimal cooling performance, particularly about the heat generated by the powertrain during the initial phases of vehicle development. However, the utilization of thermal analysis models for assessing vehicle cooling performance demands substantial computational resources, rendering them less practical for evaluating performance associated with design changes in the planning phase.

Today, the battery development process for automotive applications is relatively decoupled from the vehicle integration and system validation phase. Battery pack design targets are often disregarded at very early development phases even though they are thoroughly linked to the vehicle-level requirements such as performance, lifetime and cost. Here, AVL proposes a methodology guided by virtual testing techniques to frontload vehicle-level validation tasks in the earlier phase of battery pack testing. This paper focuses on the benefits of the methodology for both battery suppliers and automotive OEMs. Applications will be explained, based on a modular virtual testing toolchain, which involves the simulation platform and models as well as the generation of model parameters and test cases.

This paper gives insights in the theoretical measurement uncertainty of E-Drive rotor position dependent results, like Id and Iq calculations, done by a modern propulsion power analyzer (PA). The calculation of Id and Iqis fundamental to perform control optimization and application tasks for an E-Drive system. To optimize the E-Drive system application towards e.g., best efficiency, best performance, or improved NVH the importance of the testing toolchain is described: a power analyzer delivering the required results, an automation system, and a Design of Experiment tool to set improved target values. Consequently, inverters applications featuring field-oriented control (FOC) with permanent magnet synchronous machines (PMSM) are updated with a chosen control strategy. For achieving a certain behavior of an E-Drive, different degrees of freedom in the Inverter Control Unit are available; Lookup tables Id and Iq represent two fundamental application labels to be considered.

Knock in spark-ignition (SI) engines has been a subject of many research efforts and its relationship with high efficiency operating conditions keeps it a contemporary issue as engine technologies push classical limits. Despite this long history of research, literature is lacking coherent and generalized descriptions of how knock is affected by changes in the full cylinder temperature field, residence time (engine speed), and air/fuel ratio. In this work, two dimensionless numbers are applied to fully 3D SI conditions. First, the characteristic time of autoignition (ignition delay) is compared against the characteristic time of end-gas deflagration, which was used to predict knocking propensity. Second, the temperature gradient of the end-gas is compared against a critical detonation-based temperature gradient, which predicts the knock intensity.

The development of electric vehicles has been progressed, rapidly, to achieve Carbon neutrality by 2050. There have been increasing concerns about Electromagnetic Compatibility (EMC) performance due to increasing power for power trains of vehicles. Because same power train system expands to some vehicles, we have developed numerical simulations in order to predict the vehicle EMC performances. We modeled a vehicle which has inverter noises by numerical simulation to calculate electric fields based on GB/T18387. We simulated the common mode noise which flows through the shielding braid of the high voltage wire harnesses. As a result, it is confirmed a correlation between the electric fields calculated by numerical simulation and the measured one.

Combustion in a lean atmosphere diluted with a large amount of air can greatly improve fuel efficiency by reducing cooling loss [1, 2]. On the other hand, when air-fuel mixture in cylinder becomes lean, the turbulent combustion speed will decrease, resulting in problems such as the generation of unburned hydrocarbon (HC) and combustion instability [3, 4]. In order to solve these problems, it is important to increase the turbulence intensity and combustion speed [5, 6, 7, 8, 9, 10]. When designing combustion in cylinder by using Computational Fluid Dynamics (CFD), K-epsilon model is widely used for a turbulence model, and the calculated turbulence energy k or turbulence intensity u’ have been used as important indices of combustion velocity [11, 12].

It is necessary for us to reduce CO2 emissions in order to hold down global warming which is advancing year by year. Toyota Motor Corporation believes that not only the introduction of BEVs but also the sale of the hybrid vehicles must spread in order to achieve the necessary CO2 reduction. Therefore, we planned to improve the attractiveness of future hybrid vehicles. Prius has always made full use of hybrid technologies and leading to significant CO2 reduction. Toyota Motor Corporation has developed a 2.0L hybrid system for the new Prius. We built the system which could achieve a comfortable drive along following the customer’s intention while improving the fuel economy more than a conventional system. The engine improves on both output and thermal efficiency. The transaxle decreases mechanical loss by downsizing the differential, and adoption of low viscosity oil.

The safety of BEVs in driving, charging and parking condition is essential for the success of electrification in automotive industry as well as key driver of any future development of Li-Ion HV battery. AVL has developed a unique simulation approach in which the multi-physical behavior of the single cell in thermal runaway is modelled and applied to module, pack or vehicle level. In addition and beside this cell behavior, various more physical phenomena during thermal propagation on pack level are considered and predicted by the simulation method: component melting, ignition and flammibilty of venting gas and HV failures.

Brake wear is one of the dominant sources of traffic-related particulate matter emissions and is associated with various adverse environmental and health hazards. To address this issue, the UNECE mandated the Particle Measurement Program to develop a harmonized methodology for sampling and measuring brake wear particles with a full-flow sampling tunnel on a brake dynamometer. Here we present the design of a novel, fully PMP compliant sampling tunnel. The dimensions and general layout of the tunnel are based on minimization of super-micron particle losses and consideration of space limitations in brake-dynamometer setups as well as the need for efficient utilization of the test facilities (reduced testing times). Numerical calculations suggested that the critical section of the system is the sampling train from the sample probes to the instrumentation inlet/filter holder.

Nitric Oxide (NO) can significantly influence the autoignition reactivity and this can affect knock limits in conventional stoichiometric SI engines. Previous studies also revealed that the role of NO changes with fuel type. Fuels with high RON (Research Octane Number) and high Octane Sensitivity (S = RON - MON (Motor Octane Number)) exhibited monotonically retarding knock-limited combustion phasing (KL-CA50) with increasing NO. In contrast, for a high-RON, low-S fuel, the addition of NO initially resulted in a strongly retarded KL-CA50 but beyond the certain amount of NO, KL-CA50 advanced again. The current study focuses on same high-RON, low-S Alkylate fuel to better understand the mechanisms responsible for the reversal in the effect of NO on KL-CA50 beyond a certain amount of NO.

In this paper, the multi-physical simulation workflow from electromagnetics to structural dynamics for a squirrel-cage induction machine is explored. In electromagnetic simulations, local forces and rotor torque are calculated for specific speed-torque operation points. In order to consider non-linearities and interaction with control system as well as transmission, time-domain simulations are carried out. For induction machines, the computational effort with full transient numerical methods like finite element analysis (FEA) is very high. A novel reduced order electro-mechanical model is presented. It still accounts for vibro-acoustically relevant harmonics due to pulse-width modulation (PWM), slotting, distributed winding and saturation effects, but is substantially faster (minutes to hours instead of days to weeks per operation point).

Low frequency interior wind noise is typically dominated by underfloor flow noise. The source mechanisms are fluctuating surface pressure loading from both flow turbulence and acoustic field levels developed in the semi-reverberant cavity between floor and road. Previous studies have used computation fluid dynamics (CFD) to estimate the aero-acoustic loading applied to a vibro-acoustic model, which is then used to predict the transmitted interior wind noise. This paper reports a new perspective in two respects. First it uses novel surface pressure microphone arrays to directly measure the underfloor aero-acoustic loading in the wind tunnel. Second, it considers two different underfloor aerodynamic configurations - with and without lightweight aero cover panels, which are installed primarily to reduce aerodynamic drag.

HTOE (High Temperature Operation Endurance) and PTCE (Power Thermal Cycle Endurance) tests are typically performed according automotive group standards, such as LV 124 [1], VW80000 [2], FCA CS.00056 [3] or PSA B21 7130 [4]. The LV 124-2 group standard, composed by representatives of automobile manufacturers like Audi AG, BMW AG, Volkswagen AG and Porsche AG describes a wide range of environmental tests and their requirements. In addition, calculation parameters and a method are given in the standard. These group standard tests are often attributed to IEC 60068-2-2 [5] for HTOE and IEC 60068-2-14 [6] for PTCE. As both of these tests are typically of long duration, fundamentally linked to reliability (therefore requiring a statistically significant number of samples) and of considerable importance to power electronic, they are worthy of additional scrutiny for automotive developers as most automotive development moves towards electrification.

This paper presents a design method for continuous fiber composites in three-dimensional space with locally varying orientation distribution and their fabrication method. The design method is formulated based on topology optimization by augmented tensor field design variables. The fabrication method is based on Tailored Fiber Placement technology, whereby a CNC embroidery machine prepares the preform. The fiber path is generated from an optimized orientation distribution field. The preform is formed with vacuum-assisted resin transfer molding. The fabricated prototype weighs 120 g, a 70% weight reduction, achieving 3.5× mass-specific stiffness improvement.

Wet Clutches are used in automotive powertrains to enable compact designs and efficient gear shifting. During the slip phase of engagement, significant flash temperatures arise at the friction disc to separator interface because of dissipative frictional losses. An important aspect of the design process is to ensure the interface temperature does not exceed the material temperature threshold at which accelerated wear behavior and/or thermal degradation occurs. During the early stages of a design process, it is advantageous to evaluate numerous system and component design iterations exposed to plethora of possible drive cycles. A simulation tool is needed which can determine the critical operational conditions the system must survive for performance and durability to be assured. This paper describes a time-efficient multiphysics model developed to predict clutch disc temperatures with a runtime in the order of minutes.