Browse Topic: Mathematical models

Items (6,996)
ABSTRACT This document describes how to maximize the human potential/capital of your system or business by developing a Corporate Awareness. It explains and defines how Leadership, Leadership Process and human potential interrelate. It next focuses on the relation between Leadership Process and corporate culture, giving suggestions on how to develop a Corporate Self Awareness, leading to the ability to control corporate culture, in a manner that maximizes the human potential of the system/business. Next there is a discussion of solutions based on mathematical formulas and algorithms that can be implemented to define and measure Leadership Process using modern free source IT tools. Finally, this paper will provide support for aligning system/business mission/products with the Leadership Process so that Leadership maximizes the system/business human potential. It will suggest that modern IT tools be implemented as part of the solution and provide the concept of BWIKI as a solution
Thomas, Andrew O.Two, Author
ABSTRACT As contracts move from cost plus to fixed deliverables, total project cost and reducing schedules become more important. This paper will show how Model Driven Development can address common challenges in the system design, verification & testing of complex systems and systems of systems. Project success requires that hardware, software, and test teams fluently integrate application software, controlling firmware, analog and digital hardware, and mechanical components, which often proves to be costly in terms of time, money, and engineering resources. Model Driven Development and virtual prototyping using a tools flow emphasizing requirements tracing, UML / SysML system modeling, and linking to functional FPGA, IC, PCB and cabling domains supports system engineering teams along with software, digital hardware, analog hardware, system interconnect algorithm development, hardware / software co-simulation, and virtual system integration. This paper covers such solutions that
Vargas, John
ABSTRACT Single-Fuel Concept (SFC) describes the desire to operate diesel engines using JP-8 as the only fuel in the US military due to mostly logistic reasons. However, there is a lack of a fundamental database on the combustion characteristics of JP-8 compared to those studies that have been done for diesel combustion. In this current study, several kinetic models are used to look into flame properties including ignition behavior, fuel properties including evaporation characteristics, and species evolution such as soot precursor, acetylene. Several surrogates for JP-8 fuel including tetradecane, n-dodecane and a mixture of 77 vol-% n-dodecane and 23 vol-% m-xylene are selected in the model using a detailed chemical kinetic mechanism with 330 species and 1957 reactions. Included in the model are growth mechanisms of Polycyclic Aromatic Hydrocarbon (PAH), which are known to be important for soot formation. Studies are performed to describe the fundamental combustion characteristics of
Cung, Khanh D.Johnson, Jaclyn E.Zhang, AnqiNaber, Jeffrey D.Lee, Seong-Young
ABSTRACT This presentation will review the ongoing lessons learned from a joint Industry/DoD collaborative program to explore this area over the past 5 years. The discussion will review the effectiveness of integrating multiple new technologies (combined with select COTS elements) to provide a complete solution designed to reduce spares stockpiles, maximize available manpower, reduce maintenance downtime and reduce vehicle lifecycle costs. A number of new and emerging technology case studies involving diagnostic sensors (such as battery health monitors), knowledge management data accessibility, remote support-based Telematics, secure communication, condition-based software algorithms, browser-based user interfaces and web portal data delivery will be presented
Fortson, RickJohnson, Ken
ABSTRACT Due to the severity of forces exerted during an IED blast, ground vehicles undergo multiple sub-events including local structural deformation of the floor, blast-off, free flight and slam-down (including rollover). Simulation of the entire blast event is computationally intensive due to the high fidelity level of the model and the long duration of the event. The purpose of this project was to develop a computationally-efficient, reduced order model to simulate the blast event in one single simulation, to be used for rapid evaluation of military ground vehicles. Models were developed using MADYMO’s rigid body and finite element integration techniques. Different methodologies used in MADYMO simulations, their performance results and comparisons are presented. A Hybrid III 50th Percentile male ATD model, enhanced for use in vertical loading conditions, was developed and validated to drop tower tests
Chandra, SherriRamalingam, JaisankarThyagarajan, Ravi
ABSTRACT Programs have traditionally defined system requirements based on mission requirements and former system characteristics with limited knowledge on how their decisions impact the overall design space. This paper describes a methodology that combines model based systems engineering (MBSE) and multi-criteria decision-making (MCDM) to define affordable requirements prior to the design cycle. Two unmanned aerial vehicle (UAV) concepts were modeled in a multi-disciplinary simulation process environment using SIMULIA’s Process Composer application. Then the results were loaded into SIMULIA’s Results Analytics application, an advanced analytics and decision support tool, for performance versus affordability requirement trade-off analysis. Results Analytics is able to uncover data patterns, show design space sensitivity to requirements, and explicitly prioritize and quantify requirements employing a design ranking algorithm
Ceisel, JohannaKoch, PatrickVelden, Alex Van Der
Background: Road accident severity estimation is a critical aspect of road safety analysis and traffic management. Accurate severity estimation contributes to the formulation of effective road safety policies. Knowledge of the potential consequences of certain behaviors or conditions can contribute to safer driving practices. Identifying patterns of high-severity accidents allows for targeted improvements in terms of overall road safety. Objective: This study focuses on analyzing road accidents by utilizing real data, i.e., US road accidents open database called “CRSS.” It employs advanced machine learning models such as boosting algorithms such as LGBM, XGBoost, and CatBoost to predict accident severity classification based on various parameters. The study also aims to contribute to road safety by providing predictive insights for stakeholders, functional safety engineering community, and policymakers using KABCO classification systems. The article includes sections covering
Babaev, IslamMozolin, IgorGarikapati, Divya
The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video & point-cloud data. ALiVA is capable of processing large volumes of camera & lidar sensor data. Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms and annotation results review functionality. Automatic object detection functionality creates a precise bounding box around the area of interest and assigns class labels to annotated objects. Object tracking algorithms tracks detected objects in video frames, provides a unique object id for each object and
Mardhekar, AmoghPawar, RushikeshMohod, RuchaShirudkar, RohitHivarkar, Umesh N.
This document provides an overview of currently available and need to be developed modeling and simulation capabilities required for implementing robust and reliable Aerospace WDM LAN applications
AS-3 Fiber Optics and Applied Photonics Committee
In recent years, autonomous vehicles (AVs) have been receiving increasing attention from investors, automakers, and academia due to the envisioned potentials of AVs in enhancing safety, reducing emissions, and improving comfort. The crucial task in AV development boils down to perception and navigation. The research is underway, in both academia and industry, to improve AV’s perception and navigation and reduce the underlying computation and costs. This article proposes a model predictive control (MPC)-based local path-planning method in the Cartesian framework to overcome the long computation time and lack of smoothness of the Frenet method. A new equation is proposed in the MPC cost function to improve the safety in path planning. In this regard, an AV is built based on a 2015 Nissan Leaf S by modifying the drive-by-wire function and installing environment perception sensors and computation units. The custom-made AV then collected data in Norman, Oklahoma, and assisted in the
Arjmandzadeh, ZibaAbbasi, Mohammad HosseinWang, HanchenZhang, JiangfengXu , Bin
Turbocharger design involves adjustment of various geometric parameters to improve the performance and suit mechanical constraints, depending on the application-specific requirements. In designing the turbine stage, these parameters are optimized to maximize durability and efficiencies at the required operating points. For a heavy-duty class eight truck, “road load” and “rated power” are generally considered the two most important operating points. The objective of this article is to improve the efficiencies of these two operating points. The common challenge in the development of a turbine wheel design is the large number and interdependence of parameters to optimize. For example, increasing the blade thickness improves structural strength but reduces the mass flow capacity, thus influencing its performance. It is general practice to optimize the wheel geometry using iterative CFD analysis. However, running simulations for every single change in geometry involves significant
Wichlinski, JosephGonser, LukasNaik, PavanTaylor, Alexander H.Al-Hasan, Nisar S.
To understand effect of thermal hazards of LIBs during TR event, it is important to study flame propagation behaviour of LIBs during storage and transport applications. The process of flame propagation involves complex phenomena of gas phase behavior of LIBs. Present paper attempts a numerical investigation to portray this complex phenomenon. This paper investigates 18650 lithium cell considering two different chemistries NMC and LFP. A 3D numerical CFD model has been constructed to predict the gas phase behavior, threshold internal pressure, and cell gas venting of an 18650-lithium cell under thermal runaway conditions. The gas phase processes are modelled using the 4-equation thermal abuse model, while the cell's venting mechanism is modelled using Darcy's equation. Present work is divided into two parts: 1) Venting gas Internal pressure prediction 2) modeling thermal runaway event. Both procedures are implemented on two different cell chemistries to understand and evaluate following
Gudi, AbhayBonala, Sastry
A challenge of public transportation GPS data is the frequent utilization of monitoring systems with low sampling rates, primarily driven by the high costs associated with cellular data transmission of large datasets. Altitude data is often imprecise or not recorded at all in regions without large elevation changes. The low data quality limits the use of the data for further detailed investigations like a realistic energy consumption forecast for assessing the electrical grid load resulting from charging the vehicle fleet. Modern research often reconstructs speed data only, or uses additional GPS loggers, which is associated with increased costs in the vehicle fleet. The importance of precise and high-quality altitude data and specialized expertise in mountainous regions are frequently overlooked. This paper introduces an efficient new route matching method to reconstruct speed and respective road slope data of a GPS signal sampled at low frequency for a public transportation electric
Hitz, ArneKonzept, AnjaReick, BenediktRheinberger, Klaus
Eco-driving algorithms use the available information about traffic and route conditions to optimize the vehicle speed and achieve enhanced energy consumption while fulfilling a travel time constraint. Depending on what information is available, when it becomes accessible, and the level of automation of the vehicle, different energy savings can be achieved. In their basic formulation, eco-driving algorithms only leverage static information to evaluate the optimal speed, such as posted speed limits and location of stop signs. More advanced algorithms may also consider dynamic information, such as the speed of the preceding vehicle and Signal Phase and Timing of traffic lights, thus achieving higher energy efficiency. The objective of the proposed work is to develop an eco-driving algorithm that can optimize energy consumption by leveraging not only static route information, but also dynamic macroscopic traffic conditions, which are assumed to be available in real-time through
Villani, ManfrediShiledar, AnkurBlock, BrianSpano, MatteoRizzoni, Giorgio
Autonomous vehicle navigation requires signal processing of the vehicle’s sensors to provide meaningful information to the planners such that challenging artifacts like shadows, rare events, obstructive vegetation, etc. are identified properly, avoiding ill-informed navigation. Using a single algorithm such as semantic segmentation of camera images is often not enough to identify those challenging features but can be overcome by processing more than one type of sensor and fusing their results. In this work, semantic segmentation of camera image and LiDAR point cloud signals is performed using Echo State Networks to overcome the challenge of shadows identified as obstructions in off-road terrains. The coordination of algorithms processing multiple sensor signals is shown to avoid unnecessary road obstructions caused by high-contrast shadows for more informed navigational planning
Gardner, S. D.Hoxie, D.Bowen, N.Misko, S.Haider, M. R.Smereka, J.Jayakumar, P.Vantsevich, V.
A sparsely-encoded convolutional autoencoder architecture is proposed in this work for semantic segmentation of unknown terrain. The excellent feature extraction capabilities of the convolutional autoencoder (CAE) is utilized with the computation-efficient Echo State Network (ESN) for faster and efficient encoding, and semantic segmentation of unknown images. The proposed scheme manifests two CAEs trained with image and label data, and an ESN at the latent space of the two CAE to transform the encoded unknown image to semantic segmentation of different regions. The RUGD dataset of off-road images is used for training and validation of the proposed algorithm under variation of hyper-parameters. The proposed algorithm is implemented using Python and PyTorch, and simulation results demonstrate the effectiveness for semantic segmentation
Haider, Mohammad R.Hoxie, DavidGardner, StevenMisko, SamuelJayakumar, ParamsothySmereka, JonathanWoten, Jake
Resupply missions are critical logistical parts of modern warfare. Supply vehicles carrying fuel and ammunition are high-value targets meaning that the route chosen to approach such a mission is sensitive to risk and a critical time of delivery. We address the problem of a supply vehicle that needs to find a secure path to link up with a mobile frontline unit that has a fixed known itinerary. This paper presents a resupply path planning algorithm, the Adaptive Intercepting Path Planning (AIPP) algorithm, that balances risk and travel time to find the most suitable rendezvous point among several. The algorithm generates the least risky route that meets the rendezvous deadline
Damgaard, Thomas JonssonRittri, MikaelFranz, Patrick
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Goodin, ChrisCarruth, Daniel W.Dabbiru, LalithaHedrick, MichaelBlack, BrandonAspin, ZacharyCarrillo, Justin T.Kaniarz, John
Autonomous navigation in off-road terrain requires a perception system that can distinguish between vegetation that can easily be overridden and vegetation that cannot. While many autonomous systems struggle to estimate the navigability of vegetation like sparse grass or small shrubs, in this work we use a new vehicle-embedded force sensor to directly measure override forces as the vehicle drives through vegetation, allowing the perception system to learn the navigability of vegetation based on the corresponding sensor signatures. The override force can be estimated using a neural network trained on a combination of lidar and images, and the resulting force prediction can be used as an input into both local and global path-planning algorithms for autonomous navigation. In this work, we show the results for our force measurements and outline the process for extracting training data to predict override force using RESNET-50
Goodin, ChrisMoore, MarcSalmon, EthanCole, MikeJayakumar, ParamsothyEnglish, Brittney
Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Variability analyses, by sequentially sweeping across the parameter space, struggle to comprehensively assess the performance of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack’s
Samak, TanmaySamak, ChinmayKrovi, VenkatBinz, JoeyLuo, FengSmereka, JonathonBrudnak, MarkGorsich, David
In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a
Chen, ChenZheng, HongyuZong, Changfu
Cars and vans are accountable for 14.5% of the total CO2 emissions in the European Union, exerting a significant impact on public health and the environment. To align with the climate objectives set by the Council and the European Parliament, the Fit for 55 package encompasses a series of proposals aimed at revising and modernizing EU legislation while introducing new initiatives. The ultimate goal is to ensure that EU policies are in harmony with the climate targets, specifically the EU’s aspiration to reduce greenhouse gases (GHGs) by at least 55% by 2030 compared to 1990 levels and achieve climate neutrality by 2050. To meet the fleet average emissions targets, automotive Original Equipment Manufacturers (OEMs) are compelled to reduce emissions from their vehicles by addressing various components. The urgent need for car makers to reduce their carbon footprint, combined with the imperative to improve the mileage range of electric vehicles, has led to the creation of a novel
Bogliacino, FabioRe, PaoloFerrero, Alessandro
Recently, the increasing complexity of systems and diverse customer demands have necessitated the development of highly efficient vehicles. The ability to accurately predict vehicle performance through simulation allows for the determination of design specifications before the construction of test vehicles, leading to reduced development schedules and costs. Therefore, detailed brake thermal performance predictions are required both for the front and rear brakes. Moreover, scenarios requiring validation, such as alpine conditions that apply braking severity to xEV with the regenerative braking system, have become increasingly diverse. To address this challenge, this study proposes a co-simulation method that incorporates a machine-learned brake pad friction coefficient prediction model to enhance the accuracy of brake thermal capacity predictions within the vehicle simulation environment. This innovative method allows for the simultaneous prediction of both front and rear-wheel brakes
Cho, SunghyunBaek, SangHeumKim, Min SooHong, IncheolKim, Hyun KiKim, GwichulLee, Jounghee
On one hand, simulation tools are widely used to study and examine new technologies before building prototypes. It is a cost and time saver if it is mathematically modeled with and simulated in real time with sufficient fidelity. On the other hand, the expansion of electric and hybrid vehicle development requested advancing the Electronic Brake Booster (EBB) technologies. In this paper, a simulation tool for the EBB is developed to simulate the performance in real time with a very quick response compared to the previous models with a novel fuzzy logic control (FLC) for the position tracking control. The configuration of the EBB is established, and the system model, including the permanent magnet synchronous motor (PMSM), a double reduction transmission (gears and a ball screw), a servo body, a reaction disc, and the hydraulic load, is modeled. The load-dependent friction has been compensated by using the Karnopp-friction model. FLC has been used for the control algorithm. The control
Soliman, Amr M.E.Kaldas, Mina M.Soliman, Aref M.A.Huzayyin, Ahmed
This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as
Ghahremaninejad, RezaBilgen, Semih
Sensor calibration plays an important role in determining overall navigation accuracy of an autonomous vehicle (AV). Calibrating the AV’s perception sensors, typically, involves placing a prominent object in a region visible to the sensors and then taking measurements to further analyses. The analysis involves developing a mathematical model that relates the AV’s perception sensors using the measurements taken of the prominent object. The calibration process has multiple steps that require high precision, which tend to be tedious and time-consuming. Worse, calibration has to be repeated to determine new extrinsic parameters whenever either one of the sensors move. Extrinsic calibration approaches for LiDAR and camera depend on objects or landmarks with distinct features, like hard edges or large planar faces that are easy to identify in measurements. The current work proposes a method for extrinsically calibrating a LiDAR and a forward-facing monocular camera using 3D and 2D bounding
Omwansa, MarkSharma, SachinMeyer, RichardBrown, Nicholas
To expand the availability of electricity generated from nuclear power, several countries have started developing designs for small modular reactors (SMRs), which could take less time and money to construct compared to existing reactors
While semi-active suspensions help improve the ride comfort and road-holding capacity of the vehicle, they tend to be reactive and thus leave a lot of room for improvement. Incorporating road preview data allows these suspensions to become more proactive rather than reactive and helps achieve a higher level of performance. A lot of preview-based control algorithms in literature tend to require high computational effort to arrive at the optimal parameters thus making it difficult to implement in real time. Other algorithms tend to be based upon lookup tables, which classify the road input into different categories and hence lose their effectiveness when mixed types of road profiles are encountered that are difficult to classify. Thus, a novel MPC (model predictive control)-based algorithm is developed which is easy to implement online and more responsive to the varying road profiles that are encountered by the vehicle. The efficacy of the algorithm is tested against a numerical methods
Thamarai Kannan, Harish KumarFerris, John B.
The electronic mechanical brake (EMB) system is a critical actuator for achieving brake-by-wire control. This review categorizes and summarizes the literature related to EMB into three sections: actuator, mathematical modeling, and control strategies. In the actuator aspect, this article compares and analyzes motors, motion conversion mechanisms, and self-reinforcing mechanisms. For mathematical modeling, this article reviews modeling methods for EMB systems concerning motors, transmission mechanisms, friction, contact collisions, nonlinear stiffness, and hysteresis characteristics. Regarding control strategies, this article consolidates methods for clamp force control, clamp force estimation, and gap management. Finally, the article discusses potential future research directions in EMB from both hardware structure and software algorithm perspectives
Yan, ZhoudongPeng, HangChen, XinboYan, Min
Model predictive control (MPC) plays a crucial role in advancing intelligent vehicle technologies. Controllers designed based on various vehicle reference models, including kinematic and dynamic models (both linear and nonlinear), often demonstrate significant differences in control performance. This study contributes by comparing three different MPC control methods and proposing a comprehensive evaluation criterion that considers tracking accuracy, stability, and computational efficiency across various MPC designs. Joint simulations using CarSim and MATLAB/Simulink reveal distinct performance characteristics among the MPC variants. Specifically, kinematic MPC (KMPC) exhibits superior performance at low speeds, linear model predictive control (LMPC) performs best at moderate speeds, and nonlinear MPC (NMPC) achieves optimal performance at high speeds. These findings highlight the adaptive nature of MPC strategies to varying vehicle dynamics and operational conditions, emphasizing the
Lai, FeiXiao, HaoLiu, JunboHuang, Chaoqun
This article offers an algorithmic solution for moving a homogeneous platoon of position-controlled vehicles on a curved path with varying speeds and in the presence of communication losses and delays. This article considers a trajectory-based platooning with the leader–following communication topology, where the lead vehicle communicates its reference position and orientation to each autonomous follower vehicle. A follower vehicle stores this communicated information for a specific period as a virtual trail of the lead vehicle starting from the lead vehicle’s initial position and orientation. An algorithm uses this trail to find the follower vehicle’s reference position and orientation on that trail, such that the follower vehicle maintains a constant distance from the lead vehicle. The proposed algorithm helps form a platoon where each vehicle can traverse a curve with varying speeds. In contrast, in the existing literature, most of the solutions for vehicle platooning on a curved
Bhaskar, RintuWahi, PankajPotluri, Ramprasad
Ammonia-fired reciprocating engines have emerged as a promising technology in the maritime and power generation sector at medium-to-large scale (1–80 MW). The use of “on-the-fly” partial ammonia decomposition to produce a relatively small amount of hydrogen that can be used as combustion promoter, replacing fossil fuels in this function, enables this technology to provide carbon-free propulsion and power generation. In this context, it is envisioned that a hydrogen-fired prechamber ignition strategy offers significant advantages by accelerating the ammonia ignition and complete combustion process, increasing its reliability and robustness while still aiming to achieve low NO x , N2O, and NH3 emissions. This study exploits an OpenFOAM-based Large Eddy Simulation (LES) numerical modeling framework to investigate the ignition and combustion behavior of an ammonia main charge ignited by a hydrogen-fired prechamber. First, a conventional port-injection premixed configuration for the ammonia
Indlekofer, ThomasHaugen, Nils ErlandFørde, Olav ØyvindGruber, Andrea
In the highly competitive landscape of the automotive industry, enhancing ride comfort has become a paramount challenge for automakers. To address this challenge, a novel double damper suspension system has been investigated. This system, featuring two single dampers operating collaboratively as an integrated unit, is analyzed with a dual focus: a comprehensive comparison of various control algorithms to identify the one offering superior comfort and the experimental validation of these findings. The modeling process, executed in Simulink, encompasses the representation of pressure, discharge, and force equations, along with the development and testing of multiple control algorithms. The study employs a shock dynamometer, utilizing both the double damper and a single semi-active damper as test subjects in a pseudo-quarter-car test bed setup. Throughout the experimental phase, solenoid actuation in the dampers is guided by specific control logic, utilizing acceleration data for the
Hamedi, BehzadShrikanthan, SudarshanTaheri , Saied
Test cycle simulation is an essential part of the vehicle-in-the-loop test, and the deep reinforcement learning algorithm model is able to accurately control the drastic change of speed during the simulated vehicle driving process. In order to conduct a simulated cycle test of the vehicle, a vehicle model including driver, battery, motor, transmission system, and vehicle dynamics is established in MATLAB/Simulink. Additionally, a bench load simulation system based on the speed-tracking algorithm of the forward model is established. Taking the driver model action as input and the vehicle gas/brake pedal opening as the action space, the deep deterministic policy gradient (DDPG) algorithm is used to update the entire model. This process yields the dynamic response of the output end of the bench model, ultimately producing the optimal intelligent driver model to simulate the vehicle’s completion of the World Light Vehicle Test Cycle (WLTC) on the bench. The results indicate that the
Gong, XiaohaoLi, XuHu, XiongLi, Wenli
Driving safety in the mixed traffic state of autonomous vehicles and conventional vehicles has always been an important research topic, especially on highways where autonomous driving technology is being more widely adopted. The merging scenario at highway ramps poses high risks with frequent vehicle conflicts, often stemming from misperceived intentions [1]. This study focuses on autonomous and conventional vehicles in merging scenarios, where timely recognition of lane-changing intentions can enhance merging efficiency and reduce accidents. First, trajectory data of merging vehicles and their conflicting vehicles were extracted from the NGSIM open-source database in the I-80 section. The segmented cubic polynomial interpolation method and Savitzky–Golay filtering are utilized for data outlier removal and noise reduction. Second, the processed trajectory data were used as input to a hybrid Gaussian hidden Markov (GMM-HMM) model for driving intention classification, specifically lane
Ren, YouWang, XiyaoSong, JiaqiLu, WenyangLi, PenglongLi, Shangke
Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspects of electric motors. Among the different causes of the NVH issues of electrical drives, the spatial and temporal harmonics of the electrical drive system are of great importance. To reduce the tonal noise of the electric motors induced by these harmonics, harmonic injection methods are applied in many applications. However, a lot of existing researches focus more either on improving the optimization process of the harmonic injection parameter settings, or on the controller design of the harmonic injection process, while the structural dynamic characteristics of the motor are seldom considered. A lot of literature shows that the harmonic injection strategies can more effectively influence the mode 0 (M0) radial forces than the higher spatial orders, so it is more efficient to apply such methods at the frequencies/orders where the effect of mode 0 forces are dominant with respect to the
Fu, TongfangXu, ZhipengGünther, MarcoPischinger, StefanBöld, Simon
In recent years, multiple three-phase machines have become increasingly popular due to their reliability and fault tolerance, especially in the propulsion systems of ships, aircraft and vehicles. These systems greatly benefit from the robustness and efficiency offered by such machines. However, a notable challenge for these machines is that harmonics increase with the number of phases, which affects control accuracy and triggers torque oscillations. The phase shift angles between winding sets are one of the most important causes of stator current and torque harmonics. Most of conventional approaches for studying triple-three-phase or nine-phase machines focus on specific phase shifts and lack a comprehensive analysis over a range of phase shifts. This paper discusses the current and torque harmonics of triple three-phase permanent magnet synchronous machines (TTP-PMSM) with different phase shifts. The aim of this paper is to analyse and compare the effect of different phase shifts on
Li, YuShi, BufanAndert, Jakob
In the context of urban smart mobility, vehicles have to communicate with each other, surrounding infrastructure, and other traffic participants. By using Vehicle2X communication, it is possible to exchange the vehicles’ position, driving dynamics data, or driving intention. This concept yields the use for cooperative driving in urban environments. Based on current V2X-communication standards, a methodology for cooperative driving of automated vehicles in mixed traffic scenarios is presented. Initially, all communication participants communicate their dynamic data and planned trajectory, based on which a prioritization is calculated. Therefore, a decentralized cooperation algorithm is introduced. The approach of this algorithm is that every traffic scenario is translatable to a directed graph, based in which a solution for the cooperation problem is computed via an optimization algorithm. This solution is either computed decentralized by various traffic participants, who share and
Flormann, MaximilianHenze, Roman
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. For this purpose, an ASAM Open Simulation Interface (OSI
Hofrichter, KristofLinnhoff, ClemensElster, LukasPeters, Steven
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