On-Board Monitoring for EU7 – Evolution vs. Revolution
- Expert Article
Christian Martin
Senior Product Manager
Michael Weissbäck
Business Field Leader
As automotive emissions continue to stay in the focus of environmental regulations, OBM (On-Board Monitoring) emerges as a substantial instrument for ensuring emission compliance in course of the EU7 regulations. OBM systems are designed to monitor vehicle emissions in real-time, providing data to ensure that vehicles meet strict legal requirements. With the introduction of EU7 regulations, the automotive industry faces new challenges in implementing robust OBM systems capable of accurately recording and documenting emissions levels.
The precise quantification of legally regulated emissions onboard and the identification of emission influencing combinations of multiple partially damaged system components can be seen as major challenges. Here, AVL is able to offer both evolutionary and revolutionary approaches, tailored to meet the specific requirements of EU7 regulations.
The evolutionary approach is based on the traditional and existing ECU functionalities, sensors, and empirical models. Additional logics are added that make a more extensive use of the existing ECU channels for plausibility checks on system level.
The second, revolutionary approach uses virtual emission sensors with physical models and an AI methodology (“MSA”) to enable an online detection of aged system components, even if only partially damaged. With AVL’s expertise on virtualization and software development, our OBM solutions were additionally modified and adapted to the state-of-the-art ECU capabilities.
1. Ensuring the accuracy and reliability of emissions quantification under all operating conditions
This requires robust calibration and validation procedures to account for aged or defect components, fuel quality and various environmental conditions. Manufacturers must develop comprehensive development and testing methods to ensure a correct OBM systems behaviour under a wide range of operating conditions, including cold starts, transient maneuvers up to high-speed driving.
2. Integration of OBM systems with existing vehicle architectures while minimizing the impact on vehicle performance and reliability.
This involves optimizing the placement of sensors and actuators to ensure accurate measurements without compromising other vehicle functions. Manufacturers must also develop robust communication protocols and interfaces to enable seamless integration with onboard and offboard diagnostic or data systems.
3. Regulatory compliance
Additionally, it must be ensured that OBM systems are properly working and high emitting vehicles are detected and the necessary system reaction is started. On-board monitoring needs to be declared and demonstrated during type approval and also checked in In-Service Compliance Testing routines with vehicles from the field.
While the evolutionary approach offers a more incremental path towards compliance with EU7 standards, the revolutionary approach promises greater innovation and potential for advancement as well as additional relevant benefits. Ultimately, the choice between these approaches depends on various factors, including technological readiness, regulatory compliance and cost-effectiveness.
The evolutionary approach to OBM builds upon existing technologies and methodologies, leveraging established systems to meet the evolving demands of EU7 regulations. This approach emphasizes the optimization of current OBD (On-Board Diagnostics) systems, enhancing their capabilities to monitor emissions levels and detect potential issues. By reusing existing ECU (Engine Control Unit) functionalities, sensors, and empirical models, the evolutionary approach aims to streamline the transition to EU7-compliant OBM systems.
One of the primary strategies in the evolutionary approach involves refining existing sensor technologies (e.g. NOx Sensor in Diesel vehicles) to improve their accuracy and reliability in detecting emissions. Accurate modeling of emissions is important when no sensor data is available. With regard to raw emissions modeling, similar accuracy can be achieved for both engine types (gasoline and diesel engines), but there is a lack of models for tailpipe emissions stored in the control unit, especially for gasoline engines. Accurate modeling of aftertreatment systems of gasoline engines is crucial to avoid e.g. incorrect identification as a high emitting vehicle.
Additionally, the evolutionary approach focuses on optimizing software algorithms for emissions monitoring and diagnostics. This includes the development of sophisticated diagnostic routines capable of detecting deviations from expected emissions levels and identifying potential sources of malfunction or degradation. For On-Board Monitoring (OBM) purposes, continuous signal streams are needed, particularly to address cold start conditions and high-altitude operation. An evolutionary approach can act as a strategy to meet these challenges, although a revolutionary approach may also be necessary for assessing emission-influencing partial degradation.
Another key aspect of the evolutionary approach is the integration of remote monitoring and diagnostics capabilities into OBM systems. Currently, AVL is investigating, among other things, a Kalman Filter-based approach to combine emission data from virtual and real sensors, with promising results. The Kalman Filter algorithm calculates a weighting fraction based on the quality and reliability of the virtual and real sensor signals, adjusting continuously as new data becomes available. This ensures a balanced trust in both sensor inputs, leading to improved and more accurate emission results. The evaluation of the quality of the emission data is facilitated within a predefined confidence interval by a summarized estimate of the emission reliability generated by the Kalman filter. Overall, this approach enables the generation of time-resolved and distance-specific emissions, meeting OBM data provision requirements.
In contrast, the revolutionary approach seeks to push the boundaries of traditional OBD systems by incorporating virtual emission sensors and AI (Artificial Intelligence) methodologies. By leveraging advanced virtualization and software development techniques, emissions levels shall be detected in real-time and potential issues with increased accuracy and efficiency shall be identified.
One of the key innovations in the revolutionary approach is the development of virtual emission sensors that can calculate the vehicle emissions by use of simulation models. These virtual sensors utilize advanced physics-based or data-driven models to define emissions levels based on input parameters such as engine operating conditions, fuel composition, and ambient environmental factors. In addition to existing sensors, virtual emission sensors offer a cost-effective and scalable solution for OBM implementation.
Additionally, the revolutionary approach emphasizes the use of AI and machine learning algorithms for emissions monitoring and diagnostics. By analyzing large volumes of sensor data in real-time, AI-based OBM systems can detect patterns and anomalies indicative of emission violations or system malfunctions. It also enables proactive identification of potential issues before they escalate, improving the reliability and effectiveness of OBM systems in ensuring compliance with EU7 regulations. With models in place that also reflect aging effects and production scatter bands, the system is also more robust against tampering attempts.
Looking ahead, the future of OBM development lies in continued innovation and collaboration across the automotive industry. As EU7 regulations come into effect, there will be a growing need for advanced OBM solutions capable of meeting the evolving demands of emissions monitoring. In addition to Europe, also other countries as China and India intent to introduce systems comparable to OBM in the future.
One of the key areas of focus for future OBM development is the integration of predictive maintenance and optimization capabilities. By analyzing historical data and trends, OBM systems can detect potential issues before they occur and proactively recommend maintenance actions to prevent downtime and costly repairs. This not only enhances vehicle reliability and performance but also extends the lifespan of emission control systems, reducing environmental impact and lifecycle costs.
Another important direction for OBM development is the enhancement of connectivity and data analytics capabilities. By leveraging cloud-based platforms and IoT (Internet of Things) technologies, manufacturers can collect and analyze large volumes of sensor data from fleets of vehicles in real-time. This enables proactive monitoring of emissions performance across entire vehicle fleets, identifying trends and anomalies that may require corrective action. By harnessing the power of big data and machine learning, manufacturers can optimize vehicle operations and emissions control strategies to minimize environmental impact and maximize efficiency.
Additionally, there is a growing emphasis on standardization and interoperability in OBM development to achieve seamless integration with existing vehicle architectures and diagnostic systems. By adopting common protocols and interfaces, manufacturers can reduce development costs and complexity while ensuring compatibility with third-party components and aftermarket solutions. This promotes innovation and competition in the automotive industry while accelerating the adoption of advanced emissions control technologies.