## 4 Virtual Model with Predictive Consistency  
In virtual development, the construction of a vehicle model with consistency in performance prediction should be considered. In this study, to develop a coherent model, the mother car model and the measurement data of the mother car are utilized. That is, the calibration factor of the model is set using the measurement data. Then, by reflecting the specifications of the vehicle to be developed, it is used as a base model to change the chassis characteristic factor to satisfy the performance target. First, moment of inertia information, which is important for realizing the dynamic characteristics of the vehicle, was estimated. Moment of inertia information is important in realizing the dynamic characteristics of a vehicle. If there is no measured value, the regression model result is used. For an existing vehicle, measurement data were available and were used. However, as the moment of inertia of the existing vehicle was measured to be smaller than that of the regression model, the moment of inertia of the vehicle to be developed in the same class was also corrected and used by the same ratio. However, while the moment of inertia data of the existing and developed vehicles were managed based on 1 UP, only the data measured in the 0 UP state existed in the case of the competitive car. Therefore, in the trend model, the difference between the 0 UP and 1 UP states was corrected and compared with the vehicle to be developed. Therefore, it can be confirmed that the moment of inertia of the existing model is smaller than that of the vehicle to be developed, which is advantageous for handling performance (Figure 7). In this study, the chassis characteristics are optimized to secure the same or better handling performance even under conditions such as an unfavorable moment of inertia compared to competitors owing to the high development targets for collision safety and space utilization of the vehicle. To check the consistency of the model for each steering input speed, the lateral acceleration response according to the increase in the steering input frequency was compared with the measurement results. For reference, the frequency corresponding to the turn-in input during normal turns is approximately 0.5 Hz, and when changing lanes very quickly, it can reach the level of 2 Hz; therefore, 0.5-2 Hz was considered as the necessary guaranteed range. It can be observed that the response gain of the initial model is similar to the measurement result near 2 Hz, but it becomes larger than the measurement result as the frequency decreases. The main reason is that the actual car has a larger understeer value than the model. Analyzing the related phenomenon, the model is used for concept setting in the early stage of development, and therefore, the EPS logic that is tuned later for each vehicle was not reflected. However, the toe compliance characteristic of the lateral force of the front wheel that affects the understeer of the vehicle is proportional to the deformation of the torque sensor's torsion bar in the EPS. Here, the torsion bar torsion is inversely proportional to the motor assist torque, and the motor assist torque varies according to  
Figure 7: Izz (moment of inertia about the z-axis) Estimation: Trend Model + Measured Data. the vehicle or vehicle speed. Therefore, the level of understeer by EPS is different for each vehicle or each vehicle speed. Therefore, this paper proposes a method of estimating the toe compliance characteristics due to the lateral force of the front wheel when the motor assist torque is applied using the handling measurement results of the preceding vehicle (Figure 8). For estimating the toe compliance characteristics to meet the understeer level of real car, we utilized the method introduced in Section 2. Next, the lateral acceleration response according to the increase in the steering angle was compared with the measurement results to check the consistency of the model for each steering input size.