Reliability & Durability and Verifying & Testing

The validation and verification of properties and functions continue to be of particular importance in the context of product development. On the one hand, the verification of desired properties and functions is usually associated with a high cost factor. On the other hand, statements about the final component or product properties must be made as early as possible in the product development process. The former requires new, partly hybrid approaches that combine experimental and numerical methods and thus reduce complexity and test times. For the latter, it is particularly important to validate the simulative development environment used and its underlying models. Both aspects require the provision of a comprehensive test environment as well as the further development of methodological approaches for the validation and verification of complex properties and functions.

Fraunhofer Automotive bundles the necessary competencies both for the validation and verification of all properties and functions required in the automobile along the known V-model and over the utilization phase as well as for the development of new methods and approaches with regard to future vehicle concepts such as highly automated driving.

Test Methods

Test Infrastructure

1. Checking and Testing

© Fraunhofer IBP
vehicle acoustics test stand for NVH and exterior noise

Testing covers a wide range of product development tasks along the entire V-model. The main focus is on the verification of properties and functions that are necessary for a defined or required product quality. This includes

  • Complete characterization of materials (geometric, mechanical, thermal, acoustic, chemical, biological, ...) depending on the manufacturing process and use to derive input data for product design,
  • Analysis of component, assembly and vehicle properties such as (evaporation) emissions and crash and NVH behavior or comfort properties such as acoustics, interior climate and interior air quality,
  • Proof of functions whereby increasingly cyber-physical systems and IT-supported functions must be considered.

Testing includes mechanical components as well as electronics and software.


  • Determination of material characteristics under all relevant environmental influences
  • Evaluation of manufacturing influences on component and product properties
  • Non-destructive and destructive testing of mechanical and electromechanical components within the scope of quality tests and approval procedures
  • Analysis of product and system properties such as comfort, noise and emission behavior
  • Development of test methods and stands for e.g. component testing, X-the-Loop approaches or virtual testing


Project examples

2. Analysis and Demonstration of Reliability and Durability

© Fraunhofer LBF
battery test bench
Bild eines Mercedes-Benz Actros - Bei der Auslegung und Beurteilung mechanisch beanspruchter Bauteile hinsichtlich ihrer Betriebsfestigkeit/Zuverlässigkeit spielen statistische Methoden eine zentrale Rolle.
© Daimler AG/Fraunhofer ITWM
Statistical methods play a central role in the design and evaluation of mechanically stressed components with regard to their fatigue strength/reliability.

Statistical methods play a central role in the design and evaluation of mechanically stressed components with regard to their fatigue strength/reliability.

Reliability and service life are essential requirements for automotive components and systems. Reliability also includes functional safety according to VDI 26262. Reliability considerations can be applied to both technical products and processes such as manufacturing processes or supply chain management. Fraunhofer Automotive focuses primarily on the reliability and service life of automotive components and systems. Production-relevant aspects are covered by the Fraunhofer AutoMOBILE Production Alliance, among others. Within Fraunhofer Automotive, methods and processes are developed that allow an analysis and simulation of the utilization phase in order to ensure safe and reliable operation over a defined service life.

The basis for this is the classical fatigue strength, which is used for the reliable design of safety-relevant components and systems. Integrated numerical and experimental methods are used to estimate the service life. Precise knowledge of the damage mechanisms of the material, the manufacturing process and the component geometry as well as uncertainties in the development process and during the service life increase the quality of the estimation. Complementary to this, methods of system reliability such as FMEA (Failure Mode and Effects Analysis) or FTA (Fault Tree Analysis) are used, which allow for the consideration of the effect relationship and probabilities.

In addition, statistical methods play a central role in the design and evaluation of mechanically or electromechanically stressed components with regard to their fatigue strength/reliability. The process begins with the acquisition, description and modelling of the usage variability and the resulting stress, which results from the combination of the different behavior of the users with the respective environment, also using geo-referenced data. A statistical model of the strength is then required. Statistical methods that can handle small sample sizes and censored data (runs) are effective here. Finally, it is possible to make predictions for the probability of failure at the customer's site by comparing the strength with the time progressive stress.



  • Numerical and experimental reliability analyses and evaluations of electric drive trains, from electric motors to power electronics to batteries
  • Numerical and experimental lifetime assessment of mechanical or electro-mechanical components
  • Analysis of the usage phase
  • Development of methods for the analysis and evaluation of service life and reliability
  • Development of methods for the analysis and evaluation of service life and reliability
  • Provision of the JUROJIN statistics suite for planning and evaluating service life tests and predicting the probability of failure against a stress model


Project examples