*5.2. Experimental Platform Verification Experiment*

Figure 12 shows the AFPMSM tow experimental platform. Firstly, the drive test experiment is carried out, and the AFPMSM is used as the drive motor to load. The AC asynchronous motor works in the fixed speed mode, the rotation speed is 1000 rpm. After the rotation speed is stable, the given torque is 150 Nm. The A and B phase current waveforms are shown in Figure 13. The oscilloscope waveform amplitude is about 1.5 V. Then the torque tracking experiment was carried out, and the torque was abruptly changed from 0 Nm to 120 Nm. The torque waveform is shown in Figure 14.

**Figure 12.** AFPMSM tow experimental platform.

**Figure 13.** A and B phase current waveforms when the given torque is 150 Nm.

The motor drive system efficiency experiments were carried out under the torque average distribution and the torque optimal distribution control strategy based on PSO. The maximum speed of the motor is 4500 rpm. During the experiment, the motor speed is from 500 rpm to 4500 rpm, and one speed value is taken every 100 rpm for a total of 41 speed values. At every speed value, the output torque is increased from 0 Nm to 600 Nm, and a torque value is selected every 30 Nm. Twenty torque values correspond to 820 efficiency test values. The output torque *T*, the rotational speed *n*, the DC-side input voltage *U*, and the current *I* of each efficiency point are acquired by a sensor and a storage recording instrument. After calculating the efficiency of the motor drive system, the speed, output torque and efficiency values are finally imported into MATLAB to generate a motor efficiency map. The efficiency map generated by the torque average distribution and the torque optimal distribution control strategy based on the particle swarm optimization algorithm is shown in Figures 15 and 16.

**Figure 15.** System efficiency map based on traditional torque average distribution strategy.

**Figure 16.** System efficiency map of torque optimal distribution control strategy based on particle swarm optimization.

After comparison, the optimal torque distribution control strategy based on particle swarm optimization algorithm can significantly increase the high efficiency range of AFPMSM. The system efficiency increases by about 15% in the interval of 85% or more, and the system efficiency increases by 20% in the interval of 90% or more. The correctness of the proposed optimal torque distribution control strategy based on particle swarm optimization is verified.

As in the driving state, the efficiency map generated by the torque average distribution and the torque optimal distribution control strategy based on the particle swarm optimization algorithm in the braking situation is as shown in Figures 17 and 18.

**Figure 17.** System efficiency map based on traditional torque average allocation strategy in energy feedback state.

**Figure 18.** System efficiency map of torque optimal distribution control strategy based on particle swarm optimization in energy feedback state.

After comparison, the optimal torque distribution control strategy based on particle swarm optimization algorithm can significantly increase the high efficiency range of AFPMSM in energy feedback state. The system efficiency is increased by about 25% in the interval above 85%, and the system efficiency increases by 10% in the interval of 90% or more, which verifies the correctness of the optimal torque distribution control strategy in the energy feedback state.

#### *5.3. Vehicle Experiment*

As shown in Figure 19, the experimental vehicle is used to simulate the electric medium bus by means of load. The electric mid-size bus simulated in this experiment has an empty load of about 3000 kg. The battery specifications are shown in Table 1. The internal structure of the experimental vehicle is shown in Figure 20. The endurance capability based on the torque average distribution and the optimal torque distribution control strategy based on the particle swarm optimization algorithm is tested on the urban road. The load conditions are no-load and 700 kg (about 10 people). With 300 km as the target cruising range, the data in the table indicates the percentage of completion. The experimental results are shown in Table 2.

**Figure 19.** Experimental vehicle.

**Table 1.** The battery specifications.


**Figure 20.** Internal structure of the experimental vehicle.

**Table 2.** Electric medium-sized passenger car endurance experiment.


Table 2 showed that the optimal torque distribution control strategy based on PSO algorithm can improve the cruising range of 8% under no-load conditions and increase the cruising range by 8.67% under 700 kg load. Therefore, the optimal torque distribution method designed in this paper can effectively improve the system efficiency and improve the endurance.

#### **6. Conclusions**

This paper mainly studies the drive control system of electric medium bus. The selected motor is a dual-stator single-rotor AFPMSM, which can be equivalent to two PMSM connected coaxially, therefore the drive control system is divided into two parts: torque distribution and motor control. The first part is the study of the torque distribution method that maximizes the efficiency of the dual-motor system. Simulation and experimental results show that the optimal torque distribution method proposed in this paper can effectively improve system efficiency and endurance. The second part is the motor control part. Simulation and experimental results show that the system based on adaptive robust current

controller proposed in this paper has better anti-interference ability and stability than the system based on PI current controller. At the same time, this article also lays the foundation for the subsequent research of multiple modular dual-stator single-rotor combined motor systems, and provides some useful methods and ideas for the drive control system of AFPMSM.

**Author Contributions:** Conceptualization, S.W., J.Z. and T.L.; methodology, S.W. and J.Z.; software, S.W. and M.H.; validation, J.Z. and T.L.; formal analysis, J.Z. and S.W.; investigation, S.W. and M.H.; resources, J.Z. and T.L.; data curation, S.W. and M.H.; writing—Original draft preparation, J.Z., S.W. and M.H.; writing—Review and editing, J.Z., S.W. and T.L.; visualization, M.H. supervision, J.Z. and S.W.; project administration, J.Z.; funding acquisition, S.W.

**Funding:** This research was supported by the Shanghai Natural Science Foundation under Grant 19ZR1418600.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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