Vehicle functional requirements, emission regulations, and thermal limits all have a direct impact on the design of a powertrain cooling airflow system. Given the expected increase in emission-related heat rejection, suppliers and vehicle manufacturers must work together as partners in the design, selection, and packaging of cooling system components. The goal of this two-day course is to introduce engineers and managers to the basic principles of cooling airflow systems for commercial and off-road vehicles.
Autonomous vehicles rely heavily on sensors for perception, but mainly in Level 2 autonomous systems, the outputs of the sensors are used in deterministic approach. In this research paper, we propose a more sophisticated method for identifying driver inten tion by utilizing a trainable neural network based on the Transformer architecture and uses a masked Auto Encoder to analyze image sequences within a video. This allows us to predict the intention of target vehicle without the need for explicitly detecting the vehicle or other objects within the sequence using multiple models. The prediction from this model will be input to the general sensor fusion algorithm along with other deterministic parameters, paving the way to reduce the false positives and in some specific cases increase the efficiency of the current active safety functions. This end to end approach is more effective in identifying relevant objects and eliminates any latency that a multi model process could introduce.
In Crank- Train system, the prime objective of crankshaft is to facilitate the transformation of reciprocating motion of connecting rod into rotational motion at flywheel end. Moreover, the contribution of mass from crankshaft is in the same order as of Flywheel assembly mass which accounts to approximately 40 to 50% of total mass of engine. Therefore, to accomplish the development of an efficient engine it is vital to optimize the crankshaft based on simulation parameters like balance rate, mass, torsional frequency, web shear stress etc. In the given work, crankshaft has been designed and developed for an Engine used in light duty commercial vehicle. The defined work demonstrates the application of 1D Simulation tool AVL Excite in development phase of the Engine. To establish an equilibrium between the weight and simulation guidelines, many iterations of models were evaluated and finally we were able to achieve mass reduction of nearly 8% from the base model.
A potential route to reduce CO2 emissions from heavy-duty trucks is to combine low-carbon fuels and vehicle electrification/hybridization. Hybridization offers the potential to downsize the engine. Although engine downsizing in the light-duty sector can offer significant fuel economy savings mainly due to increased part-load efficiency, its benefits and downsides in heavy-duty engines are less clear. As there has been limited published research in this area to date, there is a lack of a standardized engine downsizing procedure. This paper aims to use an experimentally validated one-dimensional phenomenological combustion model in a commercial engine simulation software GT-Power alongside turbocharger scaling methods to develop downsized engines from a baseline 6-cylinder (2.2 L/cyl, 26 kW/L) pilot-ignition, direct-injection natural gas engine.
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 90deg quarter model based on motor symmetry to save the simulation time from 2 days to 4 hours. A rotor position sweep was conducted using the quarter model to find the max pulling force position. The result shows that the max pulling force happens when the rotor is 1mm overlapping with the stator core.
Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of a ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force.
In the emerging economies, there is a growing adoption of electric vehicles into fleet vehicles, especially light weight commercial vehicles, 2 wheelers and 3 wheelers. With the steady increase in this business area, there’s a demand for the innovation in the battery charging methodologies. The swappable charging method is one such charging method that’s gaining prominence. Battery swapping involves replacing an EV’s depleted battery with a fully charged one. This approach can significantly reduce wait times for drivers, as swapping batteries typically takes only a few minutes, similar to the time it takes to refuel an ICE vehicle. The objective of the present work is to optimize the charging process in the swappable charging station to prolong the battery life and also to reduce the waiting time. With battery swapping, EV owners can avoid concerns related to battery degradation, since they receive a fully charged, well-maintained battery during each swap.
In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in compressors has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence modeling (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2°and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under full operating conditions was investigated. The results showed that for the compressor performance, the turbulence models and time-step parameters selection were within 1.5% error of the simulated and measured values for pressure ratio and efficiency.
Ammonia is one of the carbon-free alternatives considered for power generation and transportation sectors. But ammonia’s lower flame speed, higher ignition energy, and higher nitrogen oxides emissions are challenges in practical applications such as internal combustion engines. As a result, modifications in engine design and control and the use of a secondary fuel to initiate combustion such as natural gas are considered for ammonia-fueled engines. The higher-octane number of methane (the main component in natural gas) and ammonia allows for higher compression ratios, which in turn would increase the engine's thermal efficiency. One simple approach to initiate and control combustion for a high-octane fuel at higher compression ratios is to use a spark plug. This study experimentally investigated the operation of a heavy-duty compression ignition engine converted to spark ignition and ammonia-methane blends.
Lithium-ion batteries (LIBs) serve as the main power source for contemporary electric vehicles (EVs). Safeguarding these batteries against damage is paramount, as it can trigger accelerated performance deterioration, potential fire hazards, environmental threats, and more. This study explores the damage progression of a commercial vehicle LIB module containing prismatic cells under crush loading. We employed computational simulations of mechanical loading tests to investigate this behavior. Physical tests involved subjecting modules to low-speed (0.05 m/s) indentations using a V-shaped stainless-steel wedge, under 6 unique loading conditions. During the tests, the force and voltage change with wedge displacement were monitored. Utilizing experimental insights, we constructed a finite element (FE) model, which included the key components of the battery module, such as the prismatic cells, steel frames and various plastic parts.
Reconstruction of inline crashes between vehicles with a low closing speed, so-called “low speed” crashes, continues to be a class of vehicle collisions that reconstructionists require specific methods to handle. In general, these collisions tend to be difficult to reconstruct due primarily to the lack of, or limited amount of, physical evidence available after the crash. Traditional reconstruction methods such as impulse-momentum (non-residual damage based) and CRASH3 (residual damage based) both are formulated without considering tire forces of the vehicles. These forces can be important in this class of collisions. An alternative stiffness-based method for low closing speed crashes has been developed [1]. This method characterizes the stiffness of vehicle pairs using data from tests with exemplars of the subject vehicles. As currently formulated, the method does not include the effects of tire forces.
For the design optimization of the electric bus body frame orienting frontal crash, considering the uncertainties may impact the crashworthiness performance, a robust optimization scheme considering tolerance design is proposed, which maps the given acceptable objective and feasibility variations into the parameter space to analyze the robustness. Two contribution analysis methods those are the entropy weight and TOPSIS method and the grey correlation calculations method are adopted to screen all the design variables, and 15 shape design variables with relatively high effect are chosen for design optimization. A symmetric tolerance and interval model is used to describe the uncertainty of 15 shape design variables of the key components of the bus body skeleton to form an uncertainty optimization problem in the form of an interval, and a triple-objective robust optimization model is developed to optimize the shape design variables and tolerances simultaneously.