1. BEIJING INSTITUTE OF TECHNOLOGY
National Engineering Laboratory for Electric Vehicles
A Novel Decoupling Control Method Based on Neural Network for EV’s Driving PMSM
Lecturer ：Wanbang Zhao
Research Field：Vehicle Engineering (electric powertrain)
Author： Wanbang Zhao,
Qiang Song (corresponding author)
Yishan Huang

2. Contents PART 01 Background PART 02 Basic theory PART 03
Control method
PART 04
Simulation
PART 05
Conclusion
PART 06
Q&A

3. 01
PART ONE
Background

4. 01 Background BIT EV development PMSM Limited resources Air pollution
Electric vehicle
PMSM
Distributive powertrain structure--Hub motor
PMSM (Permanent Magnet Synchronous Motor)
Complicated working condition
High torque control accuracy requirements
background
theory
NNPID
simulation
conclusion

5. 02 Background BIT Decoupling issue Intelligent control
PMSM —— electromagnetic coupling system
FOC decouple current as D&Q I&U of D&Q still influence with each other
Cross compensate decoupling method
Diagonal matrix decoupling method
Intelligent control
Combined intelligent control idea with FOC
AI popular AlphaGo intelligent control
fuzzy control exporter control NN control
Decoupling & self adaptiveness
background
theory
NNPID
simulation
conclusion

6. Self-adaptive control method03
Demand for EV hub motor Control method
BIT
precise
Limit of traditional PID
Complicated
condition
Complicated working condition
Self-adaptive control method
Currents coupling
Online
adjust
Self-adaptiveness
d，q
decoupling
background
theory
NNPID
simulation
conclusion

7. 02
PART TWO
Basic theory

8. 04 Theory for FOC & PMSM BIT Torque equation winding armature core
Te electromagnetic torque(Nm)
Is the current of stator(A)
ψf the rotor flux(Wb)
β　 the angle between rotor flux and the current of stator(rad)
p the number of poles
 permanent 
magnet
Nonlinear electromagnetic coupling system
theory
background
NNPID
simulation
conclusion

9. 05 Field Oriented Control（FOC） BIT Clarke & Park 01
d q axis coupling (u & i) 02
Static reference axle
theory
background
NNPID
simulation
conclusion
不完全解耦 02
矢量控制 感应电流解耦
分解转矩分量和励磁分量

10. 06 Single Neuron BIT 01 Single Neuron 02 Transfer function theory
(3.11) 01
Single Neuron 02
Transfer function
theory
background
NNPID
simulation
conclusion

11. 07 Single Neuron PID controller BIT Single Neuron PID controller 01 02
Single Neuron controller’s Recursion Training Algorithm (RTA)
theory
background
NNPID
simulation
conclusion

12. 08 BIT Single Neuron PID controller model SN PID vs PID
Result comparison
theory
background
NNPID
simulation
conclusion

13. 09 Neural Network BIT Neural Network Neural Network PID theory
background
NNPID
simulation
conclusion

14. PART THREE
Proposed method

15. 10 Decoupling control BIT 01 02 03 Coupling issue
The coupled items are ωrLqIq and ωrLdId , the coefficient is changed with speed. 02
Speed input & ideal tool
To match the coupling items, the Neural network is an ideal tool, PMSM speed is used as one of the neuron inputs to adjust the feedback weight of Id and Iq dynamically. 03
The establish of the novel method
This paper puts forward a novel neural network interaction adjustment control strategy (the NNPID method)
NNPID
background
theory
simulation
conclusion

16. 11 NNPID method BIT 02 01 Four single neuron Bring in speed factor
The four neurons achieve the negative feedback control of Id to Ud (dd neuron), Iq to Ud (qd neuron), Iq to Uq (qq neuron), and Id to Uq (dq neuron) respectively. 02
Bring in speed factor
Motor rotated speed is used as one of the inputs to adjust the coupling ratio（Wd and Wq） of each neuron in the output Ud or Uq.
NNPID
background
theory
simulation
conclusion

17. 12 NNPID method structure BIT 03 Combination
Combine the novel neural network interaction adjustment control strategy with FOC method.

18. PART FORE
Simulation

19. 13
Simulink model (comparison)
BIT

20. 14
Simulation result
BIT
Improve the torque control accuracy about 1%. the steady torque ripples is decreased by 57%
Fig. 3. speed/torque for traditional PID method (left); speed/torque the novel NNPID method (right).
simulation
background
theory
NNPID
conclusion

21. 15
Simulation result
BIT
Fig. 4. Id and Iq simulation result （comparison diagram）
simulation
background
theory
NNPID
conclusion

22. 16 Simulation result BIT Uq 权系数函数 dq神经元 KI KP qq神经元 KI KP
转速修正的 Uq控制中dq反馈与qq反馈的权值
Fig. 5. the adjustment of the each one’s PI parameters(left) and weight for neuron qq and dq (right)
simulation
background
theory
NNPID
conclusion

23. PART FIVE
Conclusion

24. 17 Conclusion BIT Shortage and expectation Decouple the d-q current
A novel
NNPID method
Decouple the d-q current
High accuracy --- Id, Iq, T
Self-adjust (dynamic speed)
Shortage and expectation
Algorithm needs large calculation time.
The learning rate can only decide by testing, too fast learning rate will lead to non-convergent and out of control.

25. THANKS FOR LISTENING

26. Q & A