Development of multifunctional protective coatings for automotive industrial applications

Project summary

In our research work, we aim to develop multifunctional protective coating formulations that meet the strict safety regulations of the automotive industry, including flame retardancy standards. Composite parts are increasingly used in electric vehicles due to their excellent mechanical properties and low weight. Weight reduction also has great environmental importance, as using composite structures can reduce carbon dioxide emissions and the energy consumption of vehicles. Currently, mainly aluminium components are used in electric vehicles, while composite structures make up only a small proportion. Using metal components obviously means higher weight and, thus, higher environmental impact. By using composite structures, this environmental impact can be significantly reduced. A major disadvantage of composites compared to metals, however, is the material's flammability, which is a significant factor in all automotive applications, but especially in electric vehicles. The flame retardancy of composite structures (whether interior elements, battery housings, etc.) can be achieved in several ways: the flame retardancy of the matrix material often leads to a reduction of mechanical properties and glass transition temperature; however, the use of multifunctional protective coatings is the most promising. Protective coatings not only provide mechanical protection but also shift the escape time and heat release during combustion in a potential fire. With the development of a suitable formulation, fire resistance can be obtained for up to minutes, which we aim to achieve in our research using synergistic additive systems. The key challenge of the proposed research programme is the development of multifunctional coating materials, which we intend to achieve by using environmentally friendly flame retardant additives based on our preliminary results, with a focus on the future mass production of components. As the development of raw materials is a time-consuming and cost-intensive process, we will use an artificial neural network-based algorithm to develop and find the ideal coating composition with significantly reduced flammability and ideal physical parameters. The further development of the model can predict the fire performance and other physical parameters based on the chemical structure of the polymer matrix in the composites (carbon atom ratio, hydrogen atom ratio, oxygen atom ratio, nitrogen atom ratio, phosphorus atom ratio, the structure of the main polymer (aliphatic, cycloaliphatic or aromatic). This would allow for preliminary screening of the polymer compositions without the need for sample preparation and small-scale testing in the future.

Project results