The Selective Laser Melting (SLM) process is employed in high-precision layer-by-layer Additive Manufacturing (AM) on powder bed and aims to fabricate high-quality structural components. To gain a comprehensive understanding of the process and its optimization, both modeling and simulation in conjunction with extensive experimental studies along with laser calibration studies have been attempted. Multiscale and multi-physics-based simulations have the potential to bring out a new level of insight into the complex interaction of laser melting, solidification, and defect formation in the SLM parts. SLM process encompasses various physical phenomena during the formation of metal parts, starting with laser beam incidence and heat generation, heat transfer, melt/fluid flow, phase transition, and microstructure solidification. To effectively model this Multiphysics problem, it is imperative to consider different scales and compatible boundary conditions in the simulations.
Electromechanical actuators (EMAs) play a crucial role in aircraft electrification, offering advantages in terms of aircraft-level weight, rigging and reliability compared to hydraulic actuators. To prevent backdriving, skewed roller braking devices called "no-backs" are employed to provide braking torque. These technology components are continuing to be improved with analysis driven design innovations eg. U.S. Pat. No. 8,393,568. The no-back mechanism has the rollers skewed around their own transverse axis that allow for a combination of rolling and sliding against the stator surfaces. This friction provides the necessary braking torque that prevents the backdriving. By controlling the friction radius and analyzing the Hertzian contact stresses, the brake can be sized for the desired duty cycle. No-backs can be configured to provide braking torque for both tensile and compressive backdriving loads.
Continuous improvements and innovations towards sustainability in the aviation industry has brought interest in electrified aviation. Electric aircrafts have short missions in which the temporal variability of thermal loads are high. Lithium-ion (Li-ion) batteries have emerged as prominent power source candidate for electric aircrafts and Urban Air Mobility (UAM). UAMs and Electric aircrafts have large battery packs with battery capacity ranging in hundreds or thousands of kWh. If the battery is exposed to temperatures outside the optimum range, the life and the performance of the battery reduces drastically. Hence, it is crucial to have a Thermal Management System (TMS) which would reduce the heat load on battery in addition to cabin, and machinery thermal loads. Thermal management can be done through active or passive cooling. Adding a passive cooling system like Phase Change Material (PCM) to the TMS reduces the design maximum thermal loads.
Hypersonic flight vehicles have potential applications in strategic defence, space missions, and future civilian high-speed transportation systems. However, structural integration has significant challenges due to extreme aero-thermo-mechanical coupled effects. Scramjet-powered air-breathing hypersonic vehicles experience extreme heat loads induced by combustion, shock waves and viscous heat dissipation. An active cooling thermal protection system for scramjet applications has the highest potential for thermal load management, especially for long-duration flights, considering the weight penalty associated with the heavier passive thermal insulation structures. We consider the case of active cooling of scramjet engine structural walls with endothermic hydrocarbon fuel. We have developed a semi-analytical quasi-2D heat transfer model considering a prismatic core single cooling channel segment as a representative volume element (RVE) to analyse larger-scale problems.
Thermal runaway is a critical safety concern in lithium-ion battery systems, emphasising the necessity to comprehend its behaviour in various modular setups. This research compares thermal runaway propagation in different modular configurations of lithium-ion batteries by analysing parameters such as cell spacing and distribution, application of phase change materials (PCMs), and implementing insulating materials. The study at the module level includes experimental validation and employs a comprehensive model considering heat transfer due to electrical performance and thermal runaway phenomena. It aims to identify the most effective modular configuration for mitigating thermal runaway risks and enhancing battery safety. The findings provide valuable insights into the design and operation of modular lithium-ion battery systems, guiding engineers and researchers in implementing best practices to improve safety and performance across various applications.
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 were integrated with other power electronics components i.e., on-board charger and auxiliary DC-DC converters as needed inside Integrated power electronics (IPE) structure to eliminate redundant high voltage connections, increase power density, and achieve higher bus voltage stability. Latest power module design from LGM using state-of-the art sintering technology and double-sided cooled structure was utilized to achieve industry leading performance and reliability.
A need to develop a cooling method with high cooling performance like jet impingement is increased as high power of an inverter is required. Jet Impingement on the dimpled plate would increase thermal performance than that of flat plate. Many previous researchers have dealt with the multi jet impingement on flat plate and some results of the study on dimpled plate evaluate the effect on heat transfer coefficients on several limited cases, making it difficult to apply them to inverter designs. Therefore, in this paper, heat transfer performance, pressure drop, and robustness at micro-scale of jet impingement on the dimpled plate were investigated in detail and the correlations of each performance were proposed. Finally, the optimal design was presented. The cooling performance was influenced by the jet array and the effect of depth and width of the dimples.
Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.
Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire. A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at a constant speed of 80 km/h.
DHT hybrid transmission assembly control system discussed in this paper includes hydraulic control, drive mode switching control, shift control, dual motor control, clutch and motor thermal management. Hydraulic control includes torque-pressure conversion. Including clutch pressure kp adaption, clutch gain adaption, clutch oil filling time adaption. Shift control includes shift type decision, shift timing control, shift torque exchange process control, shift inertia process based on motor intervention. Thermal management includes clutch flow and motor flow distribution. Motor control include the current control, mode control and boost strategy of permanent magnet synchronous motor in dual hybrid system, which has good stability and robustness. Current control includes current vector control, MTPA control, flux weakening control, PI current control and SVPWM control.
Internal combustion engine (IC engine) vehicles are commonly used for transportation due to their versatility. Due to this, efficiency in design process of IC engines is critical for the industry. To assess performance capabilities of an IC engine, thermal predictions are of utmost consequence. This study describes a computational method based on unsteady Reynolds-averaged Navier–Stokes equations that resolves the gas–liquid interface to examine the unsteady single phase/multiphase flow and heat transfer in a 4-cylinder Inline (i-4) engine. The study considers all important parts of the engine i.e., pistons, cylinder liners, head, block etc. The study highlights the ease of capturing complex and intricate flow paths with a robust mesh generation tool in combination with a robust high-fidelity interface capturing VOF scheme to resolve the gas-liquid interfaces.
Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.
In recent years, the electrification of commercial vehicles has emerged as a prominent and transformative trend within the industry. In contrast to their passenger car counterparts, commercial vehicles present distinctive challenges, necessitating solutions that address higher peak and continuous torque demands, unique packaging and interface prerequisites, and extended service life expectations. BorgWarner has committed to research and development aimed at tackling these multifaceted challenges. As a result of these efforts, BorgWarner has successfully engineered an innovative and well-balanced solution tailored specifically to the electrification of commercial vehicles. This paper describes the transformation of a Ford F-550 into a full-fledged electric vehicle employing an 800V electrical system architecture.
The growing global adoption of electric vehicles (EVs) emphasizes the pressing need for a comprehensive understanding of thermal runaway in lithium-ion batteries. Prevention of the onset of thermal runaway and its subsequent propagation throughout the entire battery pack is one of the pressing challenges of lithium-ion batteries. In addition to generating excess heat, thermal runaway of batteries also releases hazardous flammable gases, posing risks of external combustion and fires. Most existing thermal runaway models in literature primarily focus on predicting heat release or the total amount of vent gas. In this study, we present a model capable of predicting both heat release and the transient composition of emitted gases, including CO, H2, CO2, and hydrocarbons, during thermal runaway events. We calibrated the model using experimental data obtained from an 18650 cell from the literature, ensuring the accuracy of reaction parameters.
The transition towards electrification in commercial vehicles is getting more attention in recent years. This technical paper details the conversion of a production medium duty class-5 commercial truck, originally equipped with a gasoline engine and 10-speed automatic transmission, into a battery electric vehicle (BEV). The conversion process involved the removal of the internal combustion engine, transmission, and differential unit, followed by the integration of an ePropulsion system housing a newly developed dual-motor eBeam axle that propels the rear wheels. Complementary additions encompass components such as an 800V/99 kWh battery pack, advanced SiC inverters, an 800V HVAC system, and a DC fast charging system. Central to this study is the control system governing the converted vehicle, prioritizing drivability, NVH suppression, and energy optimization. Evident improvements in responsiveness and reduced noise emission underscore the efficacy of the BEV's design.
Prevailing automotive development focus shifts towards passenger-centric development of vehicle systems. Comparative to autonomous driving development, the challenge evolves to describe all relevant driving situations with the necessary information and context to be able to develop and optimize vehicle systems to actual driving situations. The situational description or scenario, i.e., context or ambience in which a vehicle is located, represents a fundamental factor in consideration of system behavior and respective system optimization opportunities. The challenge to solve the respective automotive engineering problems for nonlinear multidimensional parameter spaces or mixed integer classification problems is to describe and limit the possible solution space by suitable methodologies. Conventional methods prove inadequate solution as they can only be applied with significant financial resources and engineering time efforts, as known by autonomous driving system development.
Battery electric vehicles (BEVs) are a solution to achieving sustainability and carbon neutrality. However, BEV performances are affected by the thermal performance imbalance in the battery packs under driving cycles. Temperature changes in the module, brick, and pack under the transient cycles must be understood for model-based development. The authors conducted chassis dynamometer experiments on a fully electric small crossover sports utility vehicle (SUV) to address this challenge, examining various conditions. The testbed employed a hub-type chassis dynamometer equipped with four dynamometers, a wind blower replicating actual ambient air, and a driving aid to assist the driver according to the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Federal Test Procedures (FTP) under various ambient temperatures. The mid-size BEV with dual-motor featured 80 thermocouples mounted on the 74-kWh battery pack, including the cells, upper tray, side cover, and pack cover.