Vehicle diagnostics for the entire vehicle lifecycle: Cloud-based diagnostic services
The shift toward the software-defined vehicle (SDV) presents major challenges for vehicle calibration. The growing complexity of systems and the increasing volume of data from test fleets and customer vehicles render traditional PC-based approaches inefficient. Cloud-based toolchains enable a new form of data-driven development that transcends isolated, manual processes. In the previous issue of Hanser automotive, ETAS experts demonstrate how calibration can be made more efficient through seamless cloud integration.
In the technical article “Efficient, automated calibration via cloud-based toolchain,” Thorsten Huber and Suresh Sivavarman explain how an integrated, automated software solution can fundamentally optimize the calibration process.
Seamless workflow in the cloud
Efficiently calibrating modern vehicles requires moving individual process steps to a cloud-based environment. The goal is to create an automated, continuous workflow that encompasses the entire process, from processing raw measurement data to optimizing ECU functions. This enables automotive engineers to overcome the limitations of traditional methods.
- Automated data processing: New measurement data is automatically ingested into the cloud and processed by ETAS's Data Operator. This software consolidates raw data from various sources, unifies it, adjusts sampling rates, filters out irrelevant content, and converts it into standardized formats such as MF4. This eliminates the need for manual preprocessing on local computers while simultaneously establishing a consistent data foundation.
- Event-based analysis and report generation: Tools such as the ETAS Analytics Toolbox (EATB) automatically analyze the data, searching for specific events and conditions. The identified data sections are used for subsequent processing steps and documented in standardized reports. This ensures a structured evaluation process and traceable results.
- Automated, ML-supported calibration optimization: The relevant data is transferred to the ETAS ASCMO-MOCA tool for data-based system modeling and optimization. The tool performs an automated optimization process, calculates improved parameter sets, and stores the results as new calibration data records. With the help of machine learning (ML) algorithms, precise models can be created using only a few data points. Based on these models, optimal calibrations can be efficiently determined. Then, validation takes place in a virtual environment with tools such as EHANDBOOK or Measure Data Analyzer (MDA). The entire process can be automated to run overnight, providing engineers with a full report and the optimized results by the next morning.
A future-proof solution for SDV development
Calibrating software-defined vehicles necessitates a fundamental rethink because traditional PC-based approaches can no longer keep pace with growing data volumes and increasing system complexity. The future lies in an end-to-end, cloud-based, data-driven toolchain that spans the entire development process. This integrated approach combines automated data processing, scalable analytics, and ML-based modeling within a continuous workflow. Along with simulation and digital twins, as well as flexible orchestration between local and cloud-based execution, development teams can work more efficiently, shorten development cycles, and improve calibration quality sustainably. This effectively leverages large volumes of data and transforms them into a strategic advantage for developing software-defined vehicles.
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