# A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network

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## Abstract

**:**

## 1. Introduction

## 2. The Process of the Proposed Method

## 3. Establishing an Efficiency Map of a Propulsion Motor Using the Proposed Method

#### 3.1. Composing a Data Map of the Analyzed Motor

#### 3.2. Calculating Iron Loss Using the Harmonic Loss Method

#### 3.3. Learning Process for Flux Density Based on the NN

## 4. Results of the Proposed Method

## 5. Additional Analysis for Validation of the Proposed Method by Applying Modified Model

#### 5.1. Configuration of Modified Model

#### 5.2. Analysis and Validation of the Proposed Method

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Flowcharts for establishing an efficiency map: (

**a**) conventional method (

**b**) proposed method.

**Figure 17.**Comparison of harmonics by the FEM and learning model for the modified model: (

**a**) stator. (

**b**) rotor.

Parameter | Value |
---|---|

Poles & slots | 8 & 48 |

Stack length [mm] | 117.2 |

Outer diameter [mm] | 250 |

Steel of stator & rotor | 30PNF1600 |

Magnet grade | N46UH |

Max. current density [A${}_{rms}$/mm${}^{2}$] | 15 |

Current limit [A${}_{pk}$] | 640 |

Battery voltage [V${}_{dc}$] | 265 |

Max. speed [rpm] | 10,000 |

Parameter | Conventional Method | Proposed Method |
---|---|---|

Analyzed Points | 83 | |

Analysis Step | 21 (1/6 Period) | 61 (1/2 Period) |

Analysis Time (s) | 10 | 23 |

Total Time (s) | 830 | 1909 |

Parameters | Value | |
---|---|---|

Training set | 83 | |

Activation function | Logistic sigmoid | |

Input unit (d-q currents) | 2 | |

Output unit (Mesh elements) | Stator | 2956 |

Rotor | 5114 |

Performance | Conventional Method | Proposed Method | |
---|---|---|---|

Data map Analysis (s) | 830 | 1909 | |

Analyzed Points | 53 Points | ||

Learning Time (s) | - | 420 | |

Analysis Time (s) | 95 | 5 | |

Total Time (s) | 5865 | 2594 | |

Max. Error (%) | Iron Loss | - | 7.3 |

Efficiency | - | 0.096 |

Performance | Conventional Method | Proposed Method | |
---|---|---|---|

Data map Analysis (s) | 747 | 1660 | |

Analyzed Points | 53 Points | ||

Learning Time (s) | - | 397 | |

Analysis Time (s) | 92 | 5 | |

Total Time (s) | 5623 | 2322 | |

Max. Error (%) | Iron Loss | - | 6.5 |

Efficiency | - | 0.10 |

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## Share and Cite

**MDPI and ACS Style**

Jun, S.-B.; Kim, C.-H.; Cha, J.; Lee, J.H.; Kim, Y.-J.; Jung, S.-Y.
A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network. *Electronics* **2021**, *10*, 1049.
https://doi.org/10.3390/electronics10091049

**AMA Style**

Jun S-B, Kim C-H, Cha J, Lee JH, Kim Y-J, Jung S-Y.
A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network. *Electronics*. 2021; 10(9):1049.
https://doi.org/10.3390/electronics10091049

**Chicago/Turabian Style**

Jun, Sung-Bae, Chan-Ho Kim, JuKyung Cha, Jin Hwan Lee, Yong-Jae Kim, and Sang-Yong Jung.
2021. "A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network" *Electronics* 10, no. 9: 1049.
https://doi.org/10.3390/electronics10091049