The problem of configuring a variability model is widespread in many different domains. Renault has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles’ range hardens the space representation problem and may impact performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configuration space. The extensive experiments we conducted on datasets from Renault show our approach’s robustness and effectiveness: the achieved gain is a reduction of 52.13% in space representation and 49.81% in processing time on average.