A mechanism and data fusion-driven method for identifying the external leakage coefficient of piston chambers in coupled piston pump motor

The coupled pump-motor system, characterized by its compact structure and high-power density, is widely used in critical applications such as hydrostatic steering drives for tracked vehicles and variable-speed propulsion for construction machinery. However, external leakage of oil in the piston chamber of the pump-motor system can significantly affect the system’s performance, such as by reducing system efficiency and causing an increase in oil temperature. The external leakage characteristics of the piston chamber are influenced by the coupled effects of the dynamic and thermodynamic behavior of the friction pairs. Based on the concept of mechanism- and data-fusion-driven methodology, this study proposes an algorithm that effectively identifies and quantitatively evaluates the complex relationship between the leakage characteristics of the piston chamber and operational parameters, through the utilization of a centralized parameter model of the pump-motor system and experimental data on its external characteristics. A case study demonstrates that the algorithm can efficiently identify the external leakage coefficient across different operating conditions, with a quadratic function effectively describing the relationship between the leakage coefficient and operating parameters.