## 1 Introduction
When the motor system and its accessories are used as inertia simulation equipment, the additional resistance of the equipment is always changing due to the influence of the friction coefficient and environment (including temperature, humidity, lubricant cleanliness, etc.). In addition, the thermal change of the actuator will bring additional torque. These changes will change the transfer function of the system during operation, resulting in a poor robustness of the system [17; 18]. In Reference [19; 20], non-linear factors of the test bench are analyzed, and the effects of elasticity and backlash nonlinearity on the system performance are fully considered. Aiming at the high-order, non-linear, and multi-variable characteristics of the test-bed system, control methods suitable for each test-bed are proposed. It avoids the complex algorithm of closed-loop speed compensation and reduces the influence of inaccurate model parameters on the control accuracy of the simulation system. Literature [21] discusses the effects of inertia and the viscous friction coefficient of the load simulation system on system speed control and load simulation. The influence of inertia and the viscous friction coefficient on the test bench is analyzed through theoretical derivation and an experiment. Because of the hysteresis of motor control, the errors caused by this change can hardly be eliminated. At the same time, in the motor control part, the instantaneous forward and backward electrodynamic force of the equipment and the simplification of the system control from higher order to lower order will also bring errors to the system control [22]. Therefore, no matter what control method is adopted, the error of inertia simulation is always unavoidable. In this regard, it is very important to calibrate the load simulation system. At present, the calibration work of the vehicle load simulation system is less abundant, while the calibration methods of other load simulation systems can be used for reference. Reference [23] showcases the calibration work for a piezoelectric thrust dynamometer for small thrust liquid pulsed rocket engines. The calibration work is divided into static and dynamic parts. The static part calibrates the static output of the thrust dynamometer using high-precision piezoelectric sensors. The dynamic calibration part uses a standard steel ball to rotate at a certain frequency to determine the natural frequency of the pulse thrust dynamometer. Reference [24] presents a measurement method for the dynamic calibration of dynamometers after static calibration. However, under static and dynamic conditions, the friction loss of bearings varies, depending on the speed and torque. Therefore, the accuracy of calibration is not evaluated in this paper. Reference [25] has calibrated a force-moment measurement system for wheel-rail contact mechanics in roller rigs. In this paper, the quasi-static calibration of the measurement system is carried out by using mass blocks, and the quasi-static calibration results are fitted for dynamic measurement. Although many researchers have conducted a lot of work on the simulation of electrical inertia, the existing methods at present are limited to the calibration of the accuracy of a single sensor, and there is no system-level calibration method for a large inertia load simulator [26; 27; 28; 29; 30]. According to the electrical simulation theory of mechanical inertia, a dynamic model of a tracked vehicle under actual road conditions is established, and the control model is established considering the uncertainties and disturbances of the system. By adopting the method of using standard flywheel sets to calibrate electrical inertia, a calibration method of electrical inertia simulation accuracy for various working conditions is established, and the simulation accuracy and response time under the control strategy are calibrated.