Browse Topic: Diagnostics
The problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be
The SAE J1939 communications network is developed for use in heavy-duty environments and is suitable for horizontally integrated vehicle industries. The SAE J1939 communications network is applicable for light-duty, medium-duty, and heavy-duty vehicles used on-road or off-road, and for appropriate stationary applications which use vehicle-derived components (e.g., generator sets). Vehicles of interest include, but are not limited to, on-highway and off-highway trucks and their trailers, construction equipment, and agricultural equipment and implements. SAE J1939-71 is the SAE J1939 reference document describing SAE J1939 parameter (SP) and message (PG) definitions, SLOT (standard data encoding) definitions, conventions and notations used to specify the parameter (SP) placement in PG data, conventions for text data parameters, and conventions for PG transmission rates. This document previously contained the majority of the SAE J1939 OSI application layer data parameters and messages for
A new handheld, sound-based diagnostic system can deliver precise results in an hour with a mere finger prick of blood. The researchers used tiny particles they call functional negative acoustic contrast particles (fNACPs) and a custom-built, handheld instrument or acoustic pipette that delivers sound waves to the blood samples inside.
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems
Traditional vehicle diagnostics often rely on manual inspections and diagnostic tools, which can be time-consuming, inconsistent, and prone to human error. As vehicle technology evolves, there is a growing need for more efficient and reliable diagnostic methods. This paper introduces an innovative AI-based diagnostic system utilizing Artificial Intelligence (AI) to provide expert-level analysis and solutions for automotive issues. By inputting various details such as the vehicle’s make, model, year, mileage, problem description, and symptoms, the AI system generates comprehensive diagnostics, identifies potential causes, suggests step-by-step repair solutions, and offers maintenance tips. The proposed system aims to enhance diagnostic accuracy and efficiency, ultimately benefiting mechanics and vehicle owners. The system’s effectiveness is evaluated through various experiments and case studies, showcasing its potential to revolutionize vehicle diagnostics.
SAE J1939-73 defines the SAE J1939 messages to accomplish diagnostic services and identifies the diagnostic connector to be used for the vehicle service tool interface. Diagnostic messages (DMs) provide the utility needed when the vehicle is being repaired. Diagnostic messages are also used during vehicle operation by the networked electronic control modules to allow them to report diagnostic information and self-compensate as appropriate, based on information received. Diagnostic messages include services such as periodically broadcasting active diagnostic trouble codes, identifying operator diagnostic lamp status, reading or clearing diagnostic trouble codes, reading or writing control module memory, providing a security function, stopping/starting message broadcasts, reporting diagnostic readiness, monitoring engine parametric data, etc. California-, EPA-, or EU-regulated OBD requirements are satisfied with a subset of the specified connector and the defined messages.
In the realm of low-altitude flight power systems, such as electric vertical take-off and landing (eVTOL), ensuring the safety and optimal performance of batteries is of utmost importance. Lithium (Li) plating, a phenomenon that affects battery performance and safety, has garnered significant attention in recent years. This study investigates the intricate relationship between Li plating and the growth profile of cell thickness in Li-ion batteries. Previous research often overlooked this critical aspect, but our investigation reveals compelling insights. Notably, even during early stage of capacity fade (~ 5%), Li plating persists, leading to a remarkable final cell thickness growth exceeding 20% at an alarming 80% capacity fade. These findings suggest the potential of utilizing cell thickness growth as a novel criterion for qualifying and selecting cells, in addition to the conventional measure of capacity degradation. Monitoring the growth profile of cell thickness can enhance the
This SAE Recommended Practice supersedes SAE J1930 MAR2017 and is technically equivalent to ISO 15031-2. This document is applicable to all light-duty gasoline and diesel passenger vehicles and trucks, and to heavy-duty gasoline vehicles. Specific applications of this document include diagnostic, service and repair manuals, bulletins and updates, training manuals, repair databases, underhood emission labels, and emission certification applications. This document should be used in conjunction with SAE J1930DA Digital Annexes, which contain all of the information previously contained within the SAE J1930 tables. These documents focus on diagnostic terms applicable to electrical/electronic systems, and therefore also contain related mechanical terms, definitions, abbreviations, and acronyms. Even though the use and appropriate updating of these documents is strongly encouraged, nothing in these documents should be construed as prohibiting the introduction of a term, abbreviation, or
SAE J1939-13 specifies the diagnostic connectors used for off-board connection to a vehicle’s SAE J1939 communication links. The defined diagnostic connectors support connection to the twisted shielded pair media (refer to SAE J1939-11), the unshielded twisted pair (refer to SAE J1939-15), the twisted pair (refer to SAE J1939-14), and the twisted unshielded quad media (refer to ISO 11783-2).
A new report from Clarivate Plc, London, UK, offers a predictive analysis of high-growth medical technology markets poised to generate over $1 billion in value or achieve double-digit growth within the next five years. The report, “Medical Technologies to Watch in 2024” underscores critical areas of significant investment. Medtech analysts pinpoint five technologies driving substantial clinical and commercial value in devices and diagnostics this year. These innovations hold immense promise for patients, potentially complementing or even supplanting traditional medications and biochemical solutions. Analysts are optimistic that 2024 will bring a more favorable economic climate for medtech competitors, noting that the macro trends remain positive.
The University of Detroit Mercy Vehicle Cyber Engineering (VCE) Laboratory together with The University of Arizona is supporting Secure Vehicle Embedded Systems research work and course projects. The University of Detroit Mercy VCE Laboratory has established several testbeds to cover experimental techniques to ensure the security of an embedded design that includes: data isolation, memory protection, virtual memory, secure scheduling, access control and capabilities, hypervisors and system virtualization, input/output virtualization, embedded cryptography implementation, authentication and access control, hacking techniques, malware, trusted computing, intrusion detection systems, cryptography, programming security and secure software/firmware updates. The VCE Laboratory testbeds are connected with an Amazon Web Services (AWS) cloud-based Cyber-security Labs as a Service (CLaaS) system, which allows students and researchers to access the testbeds from any place that has a secure
With the widespread adoption of fuel cell electric vehicles, electrical insulation resistance is required for driver safety. However, there are two ways in which resistance decreases: the first is electrical shorts because of failure of high-voltage components, and the second is increased conductivity of fuel cell coolant because of depletion of ion exchange filter. In the conventional solution, since these two decreases could not be distinguished due to noise in the resistance value, a vehicle alerted customers without determining the cause and severity when the resistance value falls below a certain threshold. As a corrective maintenance, when an alert occurs, the vehicle is forced to be immediately delivered to the service center. However, in most cases where the alert came on, the cause was low-risk ion filter depletion. This resulted in customers complaining that they were startled and considering the alert to be non-threatening. As a result, the provider recommended customers to
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