Exploring the reliability of CAN-bus data in assessing forwarder rolling resistance under real working conditions

The interaction between off-road vehicles and terrain in forestry operations has been extensively studied to assess machine performance and soil damage, emphasizing the importance of the relationship between machine mobility and terrain conditions. This study assesses the rolling resistance coefficient (μr) using engine data acquired through CAN-bus systems and the J1939 standard. The aim is to determine whether soil-machine interactions can be detected by modeling rolling resistance coefficients with a simple approach based on machine parameters and essential terrain characteristics. The study was conducted on a forwarder (John Deere® 1210G) across different terrain surfaces and load conditions. CAN-bus data were processed, while terrain characteristics and slope were determined using high-accuracy spatial data. The activities consisted of (i) a calibration test to evaluate the model’s sensitivity and (ii) a field test in a real working scenario. The developed methodology demonstrated sufficient sensitivity to detect increasing rolling resistance values on rougher surfaces, highlighting the impact of surface type on forwarder operations. Field tests revealed lower rolling resistance values for the unloaded forwarder (between 0.15 and 0.3) than loaded conditions (from 0.4 to 0.6). The model reliably captured μr changes between consecutive drives and skids, particularly during uphill operations, with significant differences influenced by trail conditions and forwarder interactions rather than just load. By providing a practical methodology for assessing off-road machine performance and its impact on driving surfaces, the study highlights the importance of understanding off-road vehicle dynamics for informed operation planning decisions. This study underscores that integrating real-time mobility data from CAN-bus technology with terrain analysis enhances operational efficiency and helps minimize soil damage, thereby supporting more sustainable forest management practices.