This 4-week virtual-only experience, conducted by leading experts in the autonomous vehicle industry and academia, provides an in-depth look at the most common sensor types used in autonomous vehicle applications. By reviewing the theory, working through examples, viewing sensor data, and programming movement of a turtlebot, you will develop a solid, hands-on understanding of the common sensors and data provided by each. This course consists of asynchronous videos you will work through at your own pace throughout each week, followed by a live-online synchronous experience each Friday. The videos are led by Dr.
Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.
For the purpose of achieving carbon-neutrality in the mobility sector by 2050, hydrogen can play a crucial role as an alternative energy carrier, not only for direct usage in fuel cell-powered vehicles, but also for fueling internal combustion engines. This paper focuses on the numerical investigation of high-pressure hydrogen injection and the mixture formation inside a high-tumble engine with a conventional liquid fuel injector for passenger cars. Since the traditional 3D-CFD approach of simulating the inner flow of an injector requires a very high spatial and temporal resolution, the enormous computational effort, especially for full engine simulations, is a big challenge for an effective virtual development of modern engines. An alternative and more pragmatic lagrangian 3D-CFD approach offers opportunities for a significant reduction in computational effort without sacrificing reliability.
In electrified vehicles, auxiliary units can be a dominant source of noise, one of which is the refrigerant scroll compressor. Compared to vehicles with combustion engines, e-vehicles require larger refrigerant compressors, as in addition to the interior, also the battery and the electric motors have to be cooled. Currently, scroll compressors are widely used in the automotive industry, which generate one pressure pulse per revolution due to their discontinuous compression principle. This results in speed-dependent pressure fluctuations as well as higher-harmonic pulsations that arise from reflections. These fluctuations spread through the refrigeration cycle and cause the vibration excitation of refrigerant lines and heat exchangers. The sound transmission path in the air conditioning heat exchanger integrated in the dashboard is particularly critical. Various silencer configurations can be used to dampen these pulsations.
In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.
Opposed-piston two-stroke engines offer numerous advantages over conventional four-stroke engines, both in terms of fundamental principles and technical aspects. The reduced heat losses and large volume-to-surface area ratio inherently result in a high thermodynamic efficiency. Additionally, the mechanical design is simpler and requires fewer components compared to conventional four-stroke engines. When combining this engine concept with alternative fuels such as hydrogen and pre-chamber technology, a potential route for carbon-neutral powertrains is observed. To ensure safe engine operation using hydrogen as fuel, it is crucial to consider strict safety measures to prevent issues such as knock, pre-ignition, and backfiring. One potential solution to these challenges is the use of direct injection, which has the potential to improve engine efficiency and expand the range of load operation.
Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.
By building on mature internal combustion engine (ICE) hardware combined with dedicated hydrogen (H2) technology, the H2-ICE has excellent potential to accelerate CO2 reduction. H2-ICE concepts can therefore contribute to realizing the climate targets in an acceptable timeframe. In the landscape of H2-ICE combustion concepts, High Pressure Direct Injection (HPDI™) is an attractive option considering its high thermal efficiency, wide load range and its applicability to on-road as well as off-road heavy-duty equipment. Still, H2-HPDI is characterized by diffusion combustion, giving rise to significant NOx emissions. In this paper, the potential of H2-HPDI toward compliance with future emissions legislation is explored on a 1.8L single-cylinder research engine. With tests on multiple load-speed points, Exhaust Gas Recirculation (EGR) was shown to be an effective measure for reducing engine-out NOx, although at the cost of a few efficiency points.
This work aims at investigating the optimal configuration of an internal combustion engine fueled with bio-ethanol for improving its brake power and efficiency as well as for reducing the NOx emissions, in stationary applications. A turbocharged spark ignition engine characterized by a single-point injection was preliminarily considered; subsequently, a direct injection configuration was investigated. For both cases, a 1-D numerical model was developed to compare the injection configurations under stoichiometric conditions and different spark timings. The analysis shows that the direct injection guarantees: a limited improvement of brake power and efficiency when the same spark timing is adopted, while NOx emissions increases by 20%; an increase of 6% in brake power and 2 percentage points in brake thermal efficiency by adopting the knock limited spark advance, but an almost double NOx emissions increase.
Fossil fuels such as natural gas used in engines still play the most important role worldwide despite such measures as the German energy transition which however is also exacerbating climate change as a result of carbon dioxide emissions. One way of reducing carbon dioxide emissions is the choice of energy sources and with it a more favourable chemical composition. Natural gas, for instance, which consist mainly of methane, has the highest hydrogen to carbon ratio of all hydrocarbons, which means that carbon dioxide emissions can be reduced by up to 35% when replacing diesel with natural gas. Although natural gas engines show an overall low CO2 and pollutant emissions level, methane slip due to incomplete combustion occurs, causing methane emissions with a more than 20 higher global warming potential than CO2.
This study explores the feasibility of using a sustainable lignin-based fuel, consisting of 44 % lignin, 50 % ethanol, and 6 % water, in conventional compression ignition (CI) marine engines. Through experimental evaluations on a modified small-bore CI engine, we identified the primary challenges associated with lignin-based fuel, including engine startup and shutdown issues due to solvent evaporation and lignin solidification inside the fuel system, and deposit formation on cylinder walls leading to piston ring seizure. To address these issues, we developed a fuel switching system transitioning from lignin-based fuel to cleaning fuel with 85 vol% of acetone, 10 vol% of water and 5 vol% of ignition improving additive, effectively preventing system clogs.
In the effort to achieve the goal of a climate-neutral transportation system, the use of hydrogen and other synthetic fuels plays a key role. As battery electric vehicles become more widespread, e-fuels could be used to defossilize the hard-to-electrify transportation sectors and to store energy produced from renewable and non-continuous energy sources. Among e-fuels, hydrogen and ammonia are very attractive because they are carbon-neutral and their oxidation does not lead to any CO2 emissions. Furthermore, hydrogen/ammonia mixtures overcome the issues that arise as each of the two fuels is separately used. In the automotive sector, the use of either hydrogen, ammonia or their blends require a characterization of such mixtures under engine-like conditions, that is, at high pressures and temperatures. The aim of this work is to evaluate the Laminar Flame Speed (LFS) of hydrogen/ammonia mixtures by varying the thermodynamic conditions and the molar composition of the reactants.
The reduction of anthropogenic greenhouse gas emissions and ever stricter regulations on pollutant emissions in the transport sector require research and development of new, climate-friendly propulsion concepts. The use of renewable hydrogen as a fuel for internal combustion engines promises to provide a good solution especially for commercial vehicles. For optimum efficiency of the combustion process, hydrogen-specific engine components are required, which need to be tested on the test bench and analysed in simulation studies. This paper deals with the simulation-based investigation and optimisation of fuel injection in a 6-cylinder PFI commercial vehicle engine, which has been modified for hydrogen operation starting from a natural gas engine concept.
Water management in PEMFC power generation systems is a key point to guarantee optimal performances and durability. It is known that a poor water management has a direct impact on PEMFC voltage, both in drying and flooding conditions: furthermore, water management entails phenomena from micro-scale, i.e., formation and water transport within membrane, to meso-scale, i.e., water capillary transport inside the GDL, up to the macro-scale, i.e., water droplet formation and removal from the GFC. Water transport mechanisms through the membrane are well known in literature, but typically a high computational burden is requested for their proper simulation. To deal with this issue, the authors have developed an analytical model for the water membrane content simulation as function of stack temperature and current density, for fast on-board monitoring and control purposes, with good fit with literature data.