# Assessment of Active Wheelset Steering System Using Computer Simulations and Roller Rig Tests
## 2 Materials and Methods  
In order to compare the effectiveness of individual methods for reducing guiding forces, a simplified multibody simulation (MBS) model of an electric locomotive has been created (Figure 2). The model consists of 7 rigid bodies (car body, 2 bogie frames, and 4 wheelsets) that are connected by linear force elements. Wheel-rail contact respects non-linear characteristics of S1002 wheel and UIC 60 rail profiles, forces acting in the wheel-rail contacts are calculated using FASTSIM method [10]. The main parameters of the simulation model are listed in Table 1.  
Figure 1: Active yaw damper on Siemens Vectron MS locomotive for ÖBB, Innovrans fair 2018.  
Figure 2: Graphical representation of the simulation model.  
Figure 1: Main parameters of the simulation model.  
as MBC1, but it has less space demands. Thus, MBC2 can be utilized also on asynchronous locomotives, which have usually a large transformer located between the bogies [11].
* AYD--Active Yaw Dampers, Figure 3d. Method based on active controlled yaw torque acting between a car body and bogies. The torque is generated by a couple of linear actuators acting in between each bogie frame and car body [12; 13].
* AWS--Active Wheelset Steering, Figure 3e. Yaw angles of wheelsets towards a bogie frame are actively controlled by actuators [14; 15].  
The parameters of mechanical bogie connections MBC1 and MBC2 (i.e., stiffness and preload of coupling elements) were optimized in order to achieve the best performance in a 150 m radius of a track as well as the force produced by AYD actuators and the wheelsets yaw angle for AWS. Figure 4 and Table 2 thus expresses the theoretical maximum possible effect of reducing the guiding forces, which can be achieved by individual methods.  
It is important to note that:  
* Contribution of mechanical bogie connections MBC1 and MBC2 to the guiding force reduction will be lower than calculated values of 23 percent for MBC1, respectively 10.5 percent for MBC2. The parameters of mechanical bogie connections should be compromised in the wide range of curve radiuses.
* For AYD, the impact of forces in the actuators on the secondary suspension deflections was not taken into an account. To avoid undesired large deflections of secondary suspension in the lateral direction and transmitting forces via lateral bump-stops, the power of the actuators would probably have to be lower than considered in the simulation. Consequently, a reduction in guiding forces will be lower than the calculated 25.7%.
* The highest reduction in guiding forces (75%) shows YFS. However, such reduction is achieved for zero value of the yaw stiffness of the wheelset guidance which drastically affect the stability and lower the maximum speed of the vehicle.  
Figure 3: Vehicle configurations. YFS (**a**), MBC1 (**b**), MBC2 (**c**), AYD (**d**), and AWS (**e**).  
In the view of the above, AWS seems to be very promising method of reducing guiding forces. Therefore, the project, with the aim of design a system of active wheelset steering for an electric four-axle locomotive, was launched. The project is divided into the three main stages:  
1. computer simulations;
2. scaled roller rig experiments;
3. on track tests.  
The goal of Stage I is composing and verification of the detailed simulation model including wheelset steering actuators and control loop and optimize the wheelset steering control algorithm considering various vehicle speeds and track conditions.  
The Stage II is focused on the verification of computer simulations and demonstration of the benefits of AWS using a scaled laboratory test device [16] (Figure 5).  
The Stage III includes implementation of AWS system on an existing locomotive and performing track tests.