Search Results

Abstract An investigation into the pre-ashing of new gasoline particulate filters (GPFs) has demonstrated that the filtration efficiency of such filters can be improved by up to 30% (absolute efficiency improvement) when preconditioned using ash derived from a fuel-borne catalyst (FBC) additive. The additive is typically used in diesel applications to enable diesel particulate filter (DPF) regeneration and can be added directly into the fuel tank of the vehicle. This novel result was compared with ash derived from lube oil componentry, which has previously been shown to improve filtration efficiency in GPFs. The lube oil-derived ash utilized in this work improved the filtration efficiency of the GPF by −30%, comparable to the ash derived from the FBC additive.

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract Energy management strategies for charge-sustaining hybrid electric vehicles reduce fuel consumption and maintain battery pack state of charge while meeting driver output power demand. The equivalent consumption minimization strategy is a real-time energy management strategy that makes use of an equivalence ratio to quantify electric power consumption in terms of fuel power consumption. The magnitude of the equivalence ratio determines the hybrid electric vehicle mode of operation and influences the ability of the energy management strategy to reduce fuel consumption as well as maintain the battery pack state of charge. The equivalent consumption minimization strategy in this article uses three Willans line models, which have an associated marginal efficiency and constant offset, to model the performance in the hybrid electric vehicle controller.

Abstract Car manufacturers are continuously improving passenger comfort by advancing technologies including highly automated driving. Before the broad introduction of automated driving, specific human factors regarding passenger comfort must be considered, including motion sickness. Therefore, the identification of the frequency of motion sickness and associated factors in the population is needed to extrapolate the effects for future mobility systems. We conducted three surveys between 2015 and 2020, asking people questions about their experience with motion sickness in cars. Based on the responses of 1165 participants, gender and age showed a strong influence on the self-reported frequency of motion sickness. For deeper analysis, a logistic order regression model was used to estimate the frequency of motion sickness for different user typologies.

Abstract Water quality monitoring is needed for the effective management of water resources. Periodic sampling and regular inspection/analysis allow one to classify water and identify changes or trends in water quality over time. This article presents a novel concept of an Amphibious Hybrid Unmanned Aerial Vehicle (AHUAV) that can operate in air and water for rapid water sampling, real-time water quality analysis, and water body management. A methodology using the developed AHUAV system for water body management has also been proposed for an easier and effective way of monitoring water bodies using advanced drone technologies. Using drones for water body management can be a cost-effective and efficient way of carrying out regular inspections and continual monitoring.

Abstract Dynamic stresses exist in parts of a catalyst muffler caused by the vibration of a moving vehicle, and it is important to clarify and predict the vibration response properties for preventing fatigue failures. Assuming a vibration isolating installation in the vehicle frame, the vibration transmissibility and local dynamic stress of the catalyst muffler were examined through a vibration machine. Based on the measured data and by systematically taking vibration theories into consideration, a new prediction method of the vibration modes and parameters was proposed that takes account of vibration isolating and damping. A lumped vibration model with the six-element and one mass point was set up, and the vibration response parameters were analyzed accurately from equations of motion. In the vibration test, resonance peaks from the hanging bracket, rubber bush, and muffler parts were confirmed in three excitation drives, and local stress peaks were coordinate with them as well.

Abstract The ground-effect problems of loss of thrust and fountain-effect instabilities are quantified. Experiments to control and augment ground-effect lift and stability are presented, including jet momentum reflection and fountain redirection using various types of internal and external underbody ventral strakes. By strategically designing the vertical takeoff and landing (VTOL) ventral surface, reflection of the impinging fountain momentum is possible so that instead of losing 10% thrust while in ground-effect, remarkably, thrust is augmented 10% or more to a considerable height above the ground, in addition to stabilizing random pitch and roll moments caused by fountain instability.

Abstract The project of lean management is implemented in General Motors India Private Limited, Pune, India plant. The aim of the project is to improve manpower utilization by removing seven types of wastes using lean management system in kitting process. Lean manufacturing or management is the soul of Just-In-Time philosophy and is not new in Automobile manufacture sector where it born. Kitting area is analogs to the modern supermarket where required components, parts, consumables, subassemblies are kept in bins. These bins are placed in racks so that choosing right part at right time can be achieved easily. Video recording, in-person observation, feedback from online operators and other departments such as maintenance, control, supply chain etc. are taken. It is observed that the work content performed by current strength of operators can be performed by less number of operators. After executing this project, it was possible to reduce one operator and increase manpower utilization.

Abstract One of the key problems of battery electric vehicles is the risk of severe range reduction in winter conditions. Technologies such as heat pump systems can help to mitigate such effects, but finding adequate heat sources for the heat pump sometimes can be a problem, too. In cold ambient conditions below −10°C and for a cold-soaked vehicle this can become a limiting factor. Storing waste heat or excess cold when it is generated and releasing it to the vehicle thermal management system later can reduce peak thermal requirements to more manageable average levels. In related architectures it is not always necessary to replace existing electric heaters or conventional air-conditioning systems. Sometimes it is more efficient to keep them and support them, instead. Accordingly, we show, how latent heat storage can be used to increase the efficiency of existing, well-established heating and cooling technologies without replacing them.

Abstract As the automotive industry moves toward electrification, battery costs and vehicle range are two large issues that will delay this movement. These issues can be partially resolved through the use of series-hybrid vehicles, which can replace a portion of the batteries with a small engine that serves to recharge the battery. Given the size, weight, and operational constraints of this engine, a 2-stroke engine makes sense. Indeed, 2-stroke engines are currently being used for a number of applications including consumer products, small ground vehicles, boats, and drones. The technology has significantly improved to allow for reduced emissions and increased efficiency, especially through the use of direct injection. This article discusses the state of technology for 2-stroke engines and its application in series-hybrid vehicles. In particular, the use of a 2-stroke engine as a range extender provides significant benefit in range and cost over fully electric vehicles.

Abstract The development of a long-term sustainable hydrogen energy economy for commercial vehicle transportation will need to overcome key critical technical and logistics considerations in the near term. As compared to zero-emission powertrains, fossil-fuel-based powertrains provide mission flexibility and high uptime at a comparatively low total cost of ownership (TCO). While the incumbent carbon-intensive powertrains suffer from poor efficiency and are not sustainable to support global climate change initiatives in transportation decarbonization, techno-economic challenges continue to create complex barriers to the large-scale displacement of these with highly electrified powertrains architectures. This article specifically addresses opportunities that well-targeted subsidies would afford in achieving fuel cell electric powertrain financial parity with diesel powertrains in heavy-duty trucks (HDTs).

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract The high-frequency noise issue is one of the most significant noise, vibration, and harshness problems, particularly in battery electric vehicles (BEVs). The sound package treatment is one of the most important approaches toward solving this problem. Owing to the limitations imposed by manufacturing error, assembly error, and the operating conditions, there is often a big difference between the actual values and the design values of the sound package components. Therefore, the sound package parameters include greater uncertainties. In this article, an uncertainty analysis method for BEV interior noise was developed based on an interval model to investigate the effect of sound package uncertainty on the interior noise of a BEV. An interval perturbation method was formulated to compute the uncertainty of the BEV’s interior noise.

Abstract Gasoline Particulate Filter (GPF) technology is the key method of meeting the new regulations for particulate matter emissions from gasoline cars. Computer-Aided Engineering is widely used for the design of such systems; thus the development of accurate models for GPFs is crucial. Most existing pressure loss models require experimental calibration of several parameters. These experiments are performed at room temperatures, or on an engine test bench, where gas properties cannot be fully controlled. This article presents pressure loss measurements for clean GPF cores performed with uniform airflow and temperatures up to 680°C. The flow regime in GPF is shown to be different to that in the Diesel Particulate Filters (DPF) due to high flow rates and temperatures. Therefore, most of the existing models are not suitable for design of the new generation of aftertreatment devices. To separate pressure loss contribution from different sources, unplugged filter cores are tested.

Abstract China is driving the transition away from internal combustion engine vehicles (ICEVs) to plug-in electric vehicles (PEVs, including plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs)) to address its pressing energy security and environmental pollution problems. The recent enactment of the dual-credit scheme mandate will compensate for the phase out of the subsidy program, while ostensibly shifting the burden of filling in the cost gap between PEVs and ICEVs from the government to the automakers (though in practice to car buyers).

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract The lack of traceability in today’s supply-chain system for auto components makes counterfeiting a significant problem leading to millions of dollars of lost revenue every year and putting the lives of customers at risk. Traditional solutions are usually built upon hardware such as radio-frequency identification (RFID) tags and barcodes, and these solutions cannot stop attacks from supply-chain (insider) parties themselves as they can simply duplicate products in their local database. This industry-academia collaborative work studies the benefits and challenges associated with the use of distributed ledger (or blockchain) technology toward preventing counterfeiting in the presence of malicious supply-chain parties. We illustrate that the provision of a distributed and append-only ledger jointly governed by supply-chain parties themselves makes permissioned blockchains such as Hyperledger Fabric a promising approach toward mitigating counterfeiting.

Abstract Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles.