Browse Topic: On-board diagnostics (OBD)
This paper presents the collaborative efforts of the USCAR GPF OBD Working Group to evaluate and recommend On-Board Diagnostic (OBD) monitoring requirements for Gasoline Particulate Filters (GPFs). The group, comprising representatives from major OEMs, aims to establish a unified understanding of GPF monitoring capabilities and propose regulatory recommendations to CARB. The paper outlines the physics of soot generation and oxidation, regulatory interpretations, and diagnostic strategies, culminating in a proposed framework for GPF OBD compliance. The material in this paper was previously presented at the 2024 SAE OBD Symposium [1].
This recommended practice describes two methods for determining the tendency of interior materials used in automobiles and other vehicles to (a) produce a light scattering deposit (fog) on a glass surface, or (b) produce a measurable deposit (mass) on aluminum foil.
This study introduces a novel Large Language Model (LLM)-driven approach for comprehensive diagnosis and prognostics of vehicle faults, leveraging Diagnostic Trouble Codes (DTCs) in line with industry-standard automation protocols. The proposed model asks for significant advancement in automotive diagnostics by reasoning through the root causes behind the fault codes given by DTC document to enhance fault interpretability and maintenance efficiency, primarily for the technician and in few cases, the vehicle owner. Here LLM is trained on vehicle specific service manuals, sensor datasets, historical fault logs, and Original Equipment Manufacturer (OEM)-specific DTC definitions, which leads to context-aware understanding of the vehicle situation and correlation of incoming faults. Approach validation has been done using field level real-world vehicle dataset for different running scenarios, demonstrating model’s ability to detect complex fault chains and successfully predicting the associated root cause. By utilizing time series based future projection of the vehicle pattern, this approach could also predict the probable future faults as well as the requisite steps needed to prevent them. Overall, key contributions of this work include: (1) a modular diagnostic framework that seamlessly integrates different electronic control unit (ECU) architectures for sequential root cause analysis of vehicle faults, (2) cross-platform compatibility allowing utility across varied vehicle models and platforms, and (3) a user-friendly interface that eliminates the need for technical expertise by generating output data into simple, actionable insights. This work was benchmarked against traditional rule-based diagnostic tools and showed 50-70% reduction in the troubleshooting time for Root cause analysis (RCA). In the prognosis front, model could predict upcoming possible faults in the Battery behavior with significant accuracy. The framework also supports continuous learning by integrating new fault patterns, ensuring adaptability over time. This paper establishes the potential of integrating advanced language models into the automotive diagnostics pipeline and provides a scalable, intelligent, and intuitive solution for next-generation vehicle fault management.
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 presents a novel approach to calculate engine off time without any need for supplementary new hardware, leveraging detection of cold start to meet the new requirements for Cold start emission reduction strategy (CSERS) for OBD-II diagnostics. The proposed methodology utilizes Real time clock to estimate the time difference between a successful Engine Cranking and previous engine off to accurately estimate engine off time, enabling precise differentiation between a cold and a warm start.
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 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 many times greater than electrical losses in high-speed modes of traction machines. The paper pays special attention to monitoring the three-phase circuit of a traction machine by a measuring observer consisting of six current and voltage sensors. Algorithms for data processing using the generalized energy flow technology are presented. They allow obtaining comprehensive information on the energy state of a three-phase circuit with a time delay of no more than one millisecond. The three-phase circuit monitoring data are taken as a basis for monitoring electrical and polarization losses of the battery, electrical and switching losses of the voltage inverter, electrical, magnetic, harmonic and mechanical losses of the motor and gearbox.
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 methodologies for fuel cell diagnostic, while in the second part the features and the development process of an innovative, real-time, and on-board health and condition monitoring system, based on electrochemical impedance spectroscopy (EIS), are presented. The new innovative tool allows to detect, identify and isolate degradation, faults and non-optimal conditions. The computational performance and reliability of the diagnostic tool are tested and validated through experimental tests carried out in the laboratory on single cell and short fuel cell stack over a wide range of operating conditions and under specific sub-optimal/fault states such as drying, flooding and reactants starvation. The condition and health of PEMFC are estimated using specific health indicators for the most common root causes of faults such as drying, flooding, catalyst poisoning and anode/cathode starvation.
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 must be monitored as service degradation is more likely than a complete failure. Established monitoring and error analysis approaches for cloud environments cannot easily be transferred to vehicles. Monitored values must be aggregated and correlated to error events before cloud transmission, or suspects must be created in the vehicle for thorough analysis, reducing the data exchanged with the backend. The SOVD API provides a good foundation to diagnose service-oriented architectures and HPCs. While SOVD offers a wide range of diagnostic and monitoring features, it currently lacks solutions for diagnosing certain aspects and especially monitoring of a service-oriented architecture. This paper addresses these gaps, showcasing approaches and techniques to enhance monitoring and diagnostics.
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).
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