In the age of digital transformation, where smart systems, electric mobility, and energy efficiency are the norm rather than the exception, data is the new fuel. For projects like the ones featured on CRESCENDO, which emphasize co-simulation, system integration, and early validation in vehicle design, precise data acquisition is at the core of every technological breakthrough.
Why Data Acquisition Matters
Data acquisition (DAQ) refers to the process of collecting information that reflects the performance, condition, or behavior of systems in real-time. Whether monitoring temperatures in power electronics, measuring vibrations in electric drives, or evaluating energy consumption in hybrid vehicles, DAQ systems offer engineers the insights they need to make informed decisions.
For researchers and engineers working on sustainable and efficient transport systems—such as those targeted by EU-funded innovation projects—DAQ is essential for iterative design, accurate simulation, and validation. Without real-world data, simulations lack fidelity, and innovations risk falling short of performance, safety, or regulatory expectations.
Data Acquisition in the Automotive Sector
The automotive industry has evolved dramatically over the past decade. Electric vehicles (EVs), autonomous systems, and vehicle-to-everything (V2X) communication demand real-time data streams for performance optimization. Data acquisition technologies have adapted to this shift by becoming faster, more compact, and more intelligent.
Key applications include:
- Measuring thermal behavior of batteries and inverters
- Monitoring electromagnetic emissions
- Logging vehicle CAN, LIN, and FlexRay bus communication
- Analyzing acoustic signals for noise, vibration, and harshness (NVH)
Companies like IPETRONIK specialize in advanced data acquisition tools for mobile and laboratory environments, enabling OEMs and suppliers to validate systems under realistic conditions.
Components of a Modern DAQ System
A modern DAQ system is more than just a set of sensors connected to a computer. It includes:
- Sensors and transducers: For converting physical parameters (temperature, pressure, voltage, vibration) into electrical signals
- Signal conditioning units: For amplifying, filtering, and converting raw data into usable formats
- Data loggers: High-speed devices that collect and store data in various formats
- Software platforms: For data visualization, analysis, and export to simulation environments (e.g., MATLAB/Simulink, Modelica)
Modular systems are particularly valuable in research, allowing teams to customize data acquisition setups depending on the experiment or test scenario. The use of distributed DAQ systems also makes it easier to capture data from different parts of a vehicle or building simultaneously.
Data Analysis and Interpretation
Acquiring data is only the beginning. The true value lies in analyzing that data to derive insights, optimize systems, and support decision-making. For example, by analyzing temperature gradients across a battery pack during rapid charging, engineers can develop smarter thermal management strategies.
Modern DAQ systems are often paired with AI-powered analysis tools and cloud computing. These tools help:
- Identify patterns and anomalies in sensor data
- Generate predictive maintenance alerts
- Optimize system performance based on historical data
- Provide real-time dashboards for stakeholders
Use Case: Co-Simulation and Data Correlation
One of the biggest challenges in engineering innovation projects is correlating real-world data with simulation results. The CRESCENDO project, for example, integrates data acquisition into co-simulation workflows, helping reduce the number of physical prototypes needed.
In this context, DAQ tools need to ensure high precision, synchronization across multiple channels, and seamless data export into co-simulation tools. This supports model-based system engineering (MBSE), in which virtual models are validated and refined using real-world data.
Challenges in Data Acquisition
Despite advancements, data acquisition remains a technical challenge in several scenarios:
- Environmental noise: Electromagnetic interference can corrupt signals
- High-speed requirements: Capturing data at microsecond intervals requires sophisticated equipment
- Synchronization: Merging data from multiple sources (e.g., CAN and thermocouples) can be complex
- Scalability: Expanding from a test rig to a full vehicle fleet requires compatible, scalable systems
Overcoming these challenges involves choosing the right hardware and software—and working with experienced providers who offer robust, validated DAQ solutions.
DAQ in the Era of Sustainability and Electrification
As global efforts toward carbon neutrality accelerate, the role of DAQ in sustainability research cannot be overstated. Whether it’s monitoring energy usage in a smart grid, tracking solar panel output, or validating the performance of hydrogen fuel cells, data acquisition is critical.
Moreover, DAQ systems are increasingly being designed with sustainability in mind—low-power designs, rugged outdoor enclosures, and reusable modular components all support a more eco-friendly engineering process.
Future Trends in Data Acquisition
Looking ahead, several key trends are shaping the future of DAQ technology:
- Edge computing: Real-time processing directly on the sensor node, reducing the need to transfer massive datasets
- Wireless DAQ: Reducing cable complexity, particularly in vehicle testing
- Cloud integration: Remote access to data and dashboards for global teams
- AI-driven analytics: Enabling autonomous anomaly detection and pattern recognition
These innovations are making data acquisition more intelligent, flexible, and scalable, especially in collaborative engineering environments such as those supported by EU Horizon projects.
Conclusion
Precision data acquisition is the linchpin of modern engineering and system validation. From powertrain development to environmental simulations, collecting and analyzing the right data leads to better decisions, more efficient systems, and innovative solutions.
As projects like CRESCENDO demonstrate, integrating data acquisition into co-simulation workflows accelerates development, enhances accuracy, and reduces costs. Organizations that invest in cutting-edge DAQ systems position themselves at the forefront of innovation in mobility, energy, and beyond.