Browse Topic: On-board diagnostics (OBD)
A cold start occurs when the engine is cranked after being off for a long time, enough for its temperature to drop down to the cold ambient levels. Cold start in an engine is a critical phase as it is characterized by elevated emissions. During a cold start, exhaust components such as catalytic converter do not operate in its optimal temperature zone leading to reduced efficiency in emission control. New regulations for engine emissions are becoming stringent for this condition, hence it is important to accurately determine cold start condition in an engine to optimize the emissions control strategy. Accurate engine off time calculation plays a crucial role in cold start detection, emissions control and On-Board Diagnostics (OBD-II) decision making. This engine off time if greater than 6 hours indicates one of the conditions to confirm a cold start. Other conditions such as Ambient temperature and coolant temperature along with the engine off time confirms a cold start. This paper
This document is intended to define the standardized Diagnostic Trouble Codes (DTCs) that On-Board Diagnostic (OBD) systems in vehicles are required to report when malfunctions are detected. SAE J2012 may also be used for decoding of enhanced diagnostic DTCs and specifies the ranges reserved for vehicle manufacturer specific usage.
SAE J1979/ISO 15031-5 set includes the communication between the vehicle’s OBD systems and test equipment implemented across vehicles within the scope of the legislated emissions-related OBD. To achieve this, it is based on the Open Systems Interconnection (OSI) Basic Reference Model in accordance with ISO/IEC 7498-1 and ISO/IEC 10731, which structures communication systems into seven layers. When mapped on this model, the services specified are broken into: — Diagnostic services (layer 7), specified in: — ISO 15031-5/SAE J1979 (emissions-related OBD), — ISO 27145-3 (WWH-OBD), — Presentation layer (layer 6), specified in: — ISO 15031-2, SAE J1930-DA, — ISO 15031-5, SAE J1979-DA, — ISO 15031-6, SAE J2012-DA, — ISO 27145-2, SAE J2012-DA, — Session layer services (layer 5), specified in: — ISO 14229-2 supports ISO 15765-4 DoCAN and ISO 14230-4 DoK-Line protocols, — ISO 14229-2 is not applicable to the SAE J1850 and ISO 9141-2 protocols, — Transport layer services (layer 4), specified in
SAE J1939-81 (“Network Management”) defines the processes and messages associated with managing the addresses of applications communicating on an SAE J1939 network. Network management is concerned with the management of addresses and the association of those addresses with an actual function and with the detection and reporting of network related errors. Due to the nature of management of addresses, network management also specifies address selection and address claiming processes, requirements for reaction to brief power outages, and minimum requirements for ECUs on the network.
The deployment of PEM fuel cell systems is becoming an increasingly pivotal aspect of the electrification of the transport sector, particularly in the context of heavy-duty vehicles. One of the principal constraints to market penetration is durability of the fuel cell which hardly meets the expected targets set by the vehicle manufacturers and regulatory bodies. Over the years, researchers and companies have faced the challenge of developing reliable diagnostic and condition monitoring tools to prevent early degradation and efficiency losses of fuel cell stack. The diagnostic tools for fuel cell rely usually on model-based, data driven and hybrid approaches. Most of these are mainly developed for stationary and offline applications, with a lack of suitable methods for real-time and vehicle applications. The work presented is divided into two parts: the first part explores the main degradation conditions for a PEMFC and characteristics, advantages, and application limits of the main
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 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
To define test cases for the OBD-II interface on external test equipment (such as an OBD-II Scan Tool, Inspection/Maintenance Tester, etc.) which can be used to verify compliance with the applicable standards such as SAE J1978 and SAE J1979 for Passenger Cars, Light-Duty Trucks, and Medium-Duty Vehicles and Engines (OBD II).
This SAE Information Report describes the collection of IUMPR data required by the heavy-duty onboard diagnostic regulation 13 CCR § 1971.1 (l)(2.3.3), using SAE J1939-defined messages incorporated in a suite of software functions.
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
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
Sumitomo Rubber Industries first announced its Sensing Core technology in 2017. But it wasn't until 2024 that the Japanese tire maker used its debut appearance at CES to promote the sensor-free signal analyzer. Sumitomo president and CEO Satoru Yamamoto said the company exhibited at CES, “to expand our partner companies and to get more drivers and companies to know about this sensing core technology.”
Recent legislations require very low soot emissions downstream of the particulate filter in diesel vehicles. It will be difficult to meet the new more stringent OBD requirements with standard diagnostic methods based on differential sensors. The use of inexpensive and reliable soot sensors has become the focus of several academic and industrial works over the past decade. In this context, several diagnostic strategies have been developed to detect DPF malfunction based on the soot sensor loading time. This work proposes an advanced online diagnostic method based on soot sensor signal projection. The proposed method is model-free and exclusively uses soot sensor signal without the need for subsystem models or to estimate engine-out soot emissions. It provides a comprehensive and efficient filter monitoring scheme with light calibration efforts. The proposed diagnostic algorithm has been tested on an experimentally validated simulation platform. 2D signatures are generated from soot
Items per page:
50
1 – 50 of 851