The development of braking systems for road vehicles has undergone significant progress over time, driven by increasing requirements for vehicle safety, performance, and reliability. The operation and optimization of these systems involve the analysis of numerous parameters, such as hydraulic circuit pressure, braking component temperature, brake pad wear, and vehicle operating conditions. For the analysis and monitoring of these parameters, the Digital Twin concept represents a modern solution that enables the creation of a virtual replica of the physical system, capable of reproducing its behavior under various operating conditions.

This paper analyzes and synthesizes the main research directions identified in the scientific literature regarding the application of the Digital Twin concept in road vehicle braking systems. Current approaches include the use of this technology for monitoring operating parameters, fault diagnosis, and the development of predictive maintenance strategies.

Moreover, the ways in which Digital Twin technology can be used to develop predictive maintenance strategies are highlighted, particularly through early fault identification and the optimization of maintenance activities. The paper evaluates the major advantages of implementing this technology, such as optimizing braking system performance, increasing component lifespan, and improving data-driven decision-making processes. At the same time, current limitations are analyzed, including modeling complexity, the need for large volumes of high-quality data, and challenges related to integration into existing infrastructures. The results highlight future research directions regarding the development and application of Digital Twin technology in braking systems.