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Roller offsetting is an incremental forming technique used to generate offset stiffening or mating features in sheet metal parts. Compared to die forming, roller offsetting utilizes generic tooling to create versatile designs at a relatively lower forming speed, making it well-suited for low volume productions in automotive and other industries. However, more significant distortion can be generated from roller offset forming process resulting from springback after forming. In this work, we use particle swarm optimization to identify the tool path and resulting feature geometry that minimizes distortion. In our approach, time-dependent finite element simulations are adopted to predict the distortion of each candidate tool path using a quarter symmetry model of the part. A multi-objective fitness function is used to both minimize the distortion measure while constraining the minimal radius of curvature in the tool path.

A model-based torque control strategy which is simple and easily adaptable to various types of engines is developed in this paper. A torque model is derived from constant-volume combustion model, and applications of the model to engine torque control problem are also discussed. As examples, the torque model is calibrated with experimental data collected from two different engines, and simulation and experimental results from the torque control strategy are presented as well.

When an SI engine is equipped with individual cylinder pressure transducers, combustion timing of each cylinder can be precisely controlled by adjusting spark timing in real-time. In this paper, a novel method based on principal component analysis (PCA) is introduced to control the combustion timing with a significantly less computational burden than a conventional method.

In electric vehicle applications, the majority of the traction motors can be categorized as Permanent Magnet (PM) motors due to their outstanding performance. As indicated in the name, there are strong permanent magnets used inside the rotor of the motor, which interacts with the stator and causes strong magnetic pulling force during the assembly process. How to estimate this magnetic pulling force can be critical for manufacturing safety and efficiency. In this paper, a full 3D magnetostatic model has been proposed to calculate the baseline force using a dummy non-slotted cylinder stator and a simplified rotor for less meshing elements. Then, the full 360 deg model is simplified to a half-pole model based on motor symmetry to save the simulation time from 2 days to 2 hours. A rotor position sweep was conducted to find the maximum pulling force position. The result shows that the max pulling force happens when the rotor is 1% overlapping with the stator core.

The proposed Euro 7 regulation includes On Board Monitoring, or OBM, to continuously monitor vehicles for emission exceedances. OBM relies on feedback from existing or additional sensors to identify high emitting vehicles, which poses many challenges. Currently, sensors are not commercially available for all emissions constituents, and the accuracy of available sensors is not capable enough for in use compliance determination. On board emissions models do not offer enough fidelity to determine in use compliance and require new complex model innovation development which will be extremely complicated to implement on board the vehicle. The stack up of multi-component deterioration leading to an emissions exceedance is infeasible to detect using available sensors and models.

In this paper, we present a novel algorithm designed to accurately trigger the engine coolant flow at the optimal moment, thereby safeguarding gas-engines from catastrophic failures such as engine boil. To achieve this objective, we derive models for crucial temperatures within a gas-engine, including the engine combustion wall temperature, engine coolant-out temperature, engine block temperature, and engine oil temperature. To overcome the challenge of measuring hard-to-measure signals such as engine combustion gas temperature, we propose the use of new intermediate parameters. Our approach utilizes a lumped parameter concept with a mean-value approach, enabling precise temperature prediction and rapid simulation. The proposed engine thermal model is capable of estimating temperatures under various conditions, including steady-state or transient engine performance, without the need for extra sensors.

Electric vehicles (EV) present distinctive challenges compared to ICE (Internal Combustion Engine) powered counterparts. Cabin heating and air-conditioning stand out among them, especially cabin heating in cold weather, owing to its outsized effect on drivable range of the vehicle. Efficient management of the cabin thermal system has the potential to improve vehicle range without compromising passenger comfort. A method to improve cabin thermal system regulation by effectively leveraging the solar load on the vehicle is proposed in this work. The methodology utilizes connectivity and mapping data to predict the solar load over a future time horizon. Typically, the solar load is treated as an unmeasured external disturbance which is compensated with control. It can however be treated as an estimated quantity with potential to enable predictive control. The solar load prediction, coupled with a passenger thermal comfort model, enables preemptive thermal system control over a route.

DC fast charging (DCFC) also referred to as L3 charging, is the fastest charging technology to replenish the drivable range of an electric vehicle. DCFC provides the convenience of faster charging time compared to L1 and L2 at the expense of potentially increased battery health degradation. It is known to accelerate battery capacity fade leading to reduced range and lifetime of the EV battery. While there are active efforts and several means to reduce the downsides of DCFC at cell chemistry level, this trade-off is still an important consideration for most battery cells in automotive propulsion applications. Since DCFC is a customer driven technology, informing drivers of the trade-off of each DCFC event can potentially result in better outcomes for the EV battery life. Traditionally, the driver is advised to limit DCFC events without providing quantifiable metrics to inform their decisions during EV charging.

Hood insulators are widely used in automotive industry to improve noise insulation, pedestrian impact protection and to provide aesthetic appeal. They are attached below the hood panel and are often complex in shape and size. Pedestrian head impacts are highly dynamic events with a compressive strain rate experienced by the insulator exceeding 300/s. The energy generated by the impact is partly absorbed by the hood insulators thus reducing the head injury to the pedestrian. During this process, the insulator experiences multi-axial stress states. The insulators are usually made of soft multi-layered materials, such as polyurethane or fiberglass, and have a thin scrim layer on either side. These materials are foamed to their nominal thickness and are compression molded to take the required shape of the hood. During this process they undergo thickness reduction, thereby increasing their density.

Cast austenitic stainless steels, such as 1.4837Nb, are widely used for turbo housing and exhaust manifolds which are subjected to elevated temperatures. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of those components. Therefore, it is critical to understand the TMF behavior of the cast steels. In the present study, a series of fatigue tests including isothermal low cycle fatigue tests at elevated temperatures up to 1100°C, in-phase and out-of-phase TMF tests in the temperature ranges 100-800°C and 100-1000°C have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are discussed.

A major concern for a high-power density, heavy-duty engine is the durability of its components, which are subjected to high thermal loads from combustion. The thermal loads from combustion are unsteady and exhibit strong spatial gradients. Experimental techniques to characterize these thermal loads at high load conditions on a moving component such as the piston are challenging and expensive due to mechanical limitations. High performance computing has improved the capability of numerical techniques to predict these thermal loads with considerable accuracy. High-fidelity simulation techniques such as three-dimensional computational fluid dynamics and finite element thermal analysis were coupled offline and iterated by exchanging boundary conditions to predict the crank angle-resolved convective heat flux and surface temperature distribution on the piston of a heavy-duty diesel engine.

Complex chassis systems operate in various environments such as low-mu surfaces and highly dynamic maneuvers. The existing metrics for lateral motion hazard by Neukum [13] and Amberkar [17] have been developed and correlated to driver behavior against disturbances on straight line driving on a dry surface, but do not cover low-mu surfaces and dynamic driving scenarios which include both linear and nonlinear region of vehicle operation. As a result, an improved methodology for evaluating vehicle yaw dynamics is needed for safety analysis. Vehicle yaw dynamics safety analysis is a methodical evaluation of the overall vehicle controllability with respect to its yaw motion and change of handling characteristic.

Robust communications are crucial for autonomous military fleets. Ground vehicles function as mobile local area networks utilizing Controller Area Network (CAN) backbones. Fleet coordination between autonomous platforms relies on the Robot Operating System (ROS) publish/subscribe robotic middleware for effective operation. To bridge communications between the CAN and ROS network segments, the CAN2ROS bridge software supports bidirectional data flow with message mapping and node translation. Fuzzing, a software testing technique, involves injecting randomized data inputs into the target system. This method plays a pivotal role in identifying vulnerabilities. It has proven effective in discovering vulnerabilities in online systems, such as the integrated CAN/ROS system. In our study, we consider ROS implementing zero-trust access control policies, running on a Gazebo test-bed connected to a CAN bus.

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network.

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

The subsystem of front of dash (FOD) and instrument panel (IP) is a critical path to isolate the powertrain noise and road noise for vehicles. This subsystem mainly consists of sheet metal, dash mats, IP, and the components inside IP such as HVAC and wiring harness. To achieve certain level of cabin quietness, the sound transmission loss performance of this subsystem is usually used as a quantifier. In this paper, the sound transmission loss through the FOD and IP is investigated up to 10kHz, through both acoustic testing and numerical simulation. In the acoustic testing, the subsystem is cut from a vehicle and installed on the wall of two-rooms STL testing suite, with source room being reverberant and receiver room being anechoic. In the testing, various scenarios are measured to understand the contributions from different components.

The study focuses on understanding the air and oil flow characteristics within a ball bearing during high-speed rotation, with a particular emphasis on optimizing frictional heat dissipation and oil lubrication methods. Computational fluid dynamics (CFD) techniques are employed to analyze the intricate three-dimensional airflow and oil flow patterns induced by the motion of rotating and orbiting balls within the bearing. A significant challenge in conducting three-dimensional CFD studies lies in effectively resolving the extremely thin gaps existing between the balls, races, and cages within the bearing assembly. In this research, we adopt the ball-bearing structured meshing strategy offered by Simerics-MP+ to meticulously address these micron-level clearances, while also accommodating the rolling and rotation of individual balls. Furthermore, we investigate the impact of different designs of the lubrication ports to channel oil to other locations compared to the ball bearings.

Electric motor whine is a major NVH source for electric vehicles. Traditional mitigation methods focus on e-motor hardware optimization, which requires long development cycles and may not be easily modified when the hardware is built. This paper presents a control- and software-based strategy to reduce the most dominant motor order of an IPM motor for General Motors’ Ultium electric propulsion system, using the patented active Torque Ripple Cancellation (TRC) technology with harmonic current injection. TRC improves motor NVH directly at the source level by targeting the torque ripple excitations, which are caused by the electromagnetic harmonic forces due to current ripples. Such field forces are actively compensated by superposition of a phase-shifted force of the same spatial order by using of appropriate current.

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants.

With the advent of this new era of electric-driven automobiles, the simulation and virtual digital twin modeling world is now embarking on new sets of challenges. Getting key insights into electric motor behavior has a significant impact on the net output and range of electric vehicles. In this paper, a complete 3D CFD model of an Electric Motor is developed to understand its churning losses at different operating speeds. The simulation study details how the flow field develops inside this electric motor at different operating speeds and oil temperatures. The contributions of the crown and weld endrings, crown and weld end-windings, and airgap to the net churning loss are also analyzed. The oil distribution patterns on the end-windings show the effect of the centrifugal effect in scrapping oil from the inner structures at higher speeds. Also, the effect of the sump height with higher operating speeds are also analyzed.