
Figure 1.
Diagram of the traction system of an EV based on a MGIM. EV, electric vehicle; MGIM, magnetically geared induction motor.
Table 1.
Parameters of the MGIM dynamic model.
| Parameter | Definition |
|---|---|
| ωm, ωL | Angular speed of the motor, load |
| Jm, JL, Jg | Moment of inertia of the rotor, load, gear |
| Te, TL, Tg | Torque of the rotor, load, gear |
| ϕ | Angle denoting the speed difference between rotor and load |
| Gr | Transmission ratio of the magnetic gear |
| Bm, BL, Bg | Friction coefficient at rotor, load and gear |
| po, pm | Number of ferromagnetic pole pieces and air gaps |
| nL | Sum of ferromagnetic pole pieces and air gaps |
| isd, isq | d,q axis components of the IM stator currents |
| Rs, Rr | Resistance of the IM’s stator, rotor |
| ψrd, ψrq | d,q axis components of the IM rotor flux |
| Ls, Lr | Inductance of the IM’s stator, rotor |
| M | Mutual inductance between IM’s stator and rotor |
| np | Number of poles of the IM’s stator |
| ρ | Orientation of the IM’s magnetic field |
| α, β, γ | |
| μ, σ |

Figure 2.
Diagram of the control scheme for the MGIM. MGIM, magnetically geared induction motor.

Figure 3.
Tracking of setpoint 1 by the MGIM with the use of non-linear optimal control: (a) convergence of state variables x1 to x3 (blue lines) to the associated setpoints (red lines) and estimated values provided by Kalman Filtering (b) convergence of state variables x4 to x6 (blue lines) to the associated setpoints (red lines) and estimated values provided by Kalman Filtering. MGIM, magnetically geared induction motor.

Figure 4.
Tracking of setpoint 1 by the MGIM with the use of non-linear optimal control: (a) variations of the control inputs u1 and u2 (blue lines) (b) variation of the tracking error variables ei, i = 1,…,6 associated with the state variables xi, i = 1,…,6. MGIM, magnetically geared induction motor.

Figure 5.
Tracking of setpoint 2 by the MGIM with the use of non-linear optimal control: (a) convergence of state variables x1 to x3 (blue lines) to the associated setpoints (red lines) and estimated values provided by Kalman Filtering (b) convergence of state variables x4 to x6 (blue lines) to the associated setpoints (red lines) and estimated values provided by Kalman Filtering. MGIM, magnetically geared induction motor.

Figure 6.
Tracking of setpoint 2 by the MGIM with the use of non-linear optimal control: (a) variations of the control inputs u1 and u2 (blue lines) (b) variation of the tracking error variables ei, i = 1,…,6 associated with the state variables xi, i = 1,…,6. MGIM, magnetically geared induction motor.
Table 2.
Tracking RMSE for the MGIM in the disturbance-free case
| RMSEx1 | RMSEx2 | RMSEx3 | RMSEx4 | RMSEx5 | RMSEx6 | |
|---|---|---|---|---|---|---|
| Test1 | 0.0052 | 0.0026 | 0.0064 | 0.0037 | 0.0001 | 0.0002 |
| Test2 | 0.0041 | 0.0020 | 0.0064 | 0.0063 | 0.0002 | 0.0003 |
Table 3.
Tracking RMSE for the MGIM in the case of disturbances
| Δa% | RMSEx1 | RMSEx2 | RMSEx3 | RMSEx4 | RMSEx5 | RMSEx6 |
|---|---|---|---|---|---|---|
| 0% | 0.0052 | 0.0026 | 0.0064 | 0.0037 | 0.0001 | 0.0002 |
| 10% | 0.0057 | 0.0029 | 0.0064 | 0.0014 | 0.0001 | 0.0003 |
| 20% | 0.0062 | 0.0031 | 0.0064 | 0.0007 | 0.0001 | 0.0003 |
| 30% | 0.0066 | 0.0033 | 0.0064 | 0.0027 | 0.0002 | 0.0001 |
| 40% | 0.0069 | 0.0035 | 0.0065 | 0.0046 | 0.0002 | 0.0003 |
| 50% | 0.0073 | 0.0036 | 0.0065 | 0.0064 | 0.0002 | 0.0003 |
| 60% | 0.0075 | 0.0038 | 0.0065 | 0.0081 | 0.0002 | 0.0003 |