Remote monitoring of commercial vehicles is taking an increasingly central position in automotive companies, driven by the growth of the on-road freight transportation sector. Specifically, telematics devices are increasingly gaining importance in monitoring powertrain operability, performance, reliability, sustainability, and maintainability. These systems enable real-time data collection and analysis, offering valuable support in resolving issues that may occur on the road. Moreover, the fault codes, called Diagnostic Trouble Codes (DTCs), that arise during actual road driving constitute fundamental information when combined with several engine parameters updated every second. This integration provides a more accurate assessment of vehicle conditions, allowing proactive maintenance strategies. The principal goal is to deliver an even faster response for resolving sudden issues, thus minimizing vehicle downtime. High-resolution data transmission and failure event information facilitates the bench simulation of actual missions. Precisely, a real-world mission affected by a DTC and characterized by DPF active regeneration was replicated on a test bench using telematics data. Engine behavior has been reproduced through recorded engine speed and pedal position traces, enabling comparison with the original event. A map-based model, derived from telematics data, has been then developed to estimate DPF soot loading level. Starting from two pre-existing maps, an experimental campaign allows the definition of an additional map, enabling the model to closely match the signal of the soot mass amount provided by the ECU. It represents a proprietary value not accessible via telematics. Additionally, to further reduce mission dependency, a correlation based on the same key variables has been formulated, and a good agreement is highlighted. Therefore, the scope of the activity is to investigate the formulation of a Telematics-Based model that provides a diagnostic-relevant estimation using only accessible signals.