Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

 

The optimization of traction and steering motors of an autonomous electric shuttle under driving scenarios was studied in this paper. The research ideas of this paper are of great significance to the design and development of automotive motors, and are highly recommended for publication.

In line 84 and line 88, the same sentence of “Figure 2 shows the dynamic simulation model of this vehicle. Here, the study was carried out on a 4-wheel drive (dual track) vehicle model with 3 degrees of freedom.” is repeated.

Comments on the Quality of English Language

No problem with the language.

Author Response

Dear Editor and Reviewer,

Please find attachment (Author's Reply to the Review Report (Reviewer 1)).

Best Regards

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this article, an examination of traction and steering performances is conducted based on various driving scenarios for self-driving shuttle vehicles. Operational scenarios, comprising reference speed and steering signals, are applied to a dynamic vehicle model to establish minimum performance benchmarks for traction and steering motors. Subsequently, utilizing Design of Experiments (DoE), the identified design parameters are incorporated into two distinct Artificial Neural Network (ANN) models tailored for motor performance. These ANN models, trained based on minimum performance criteria, are then employed to predict motor models with updated design parameters for both traction and steering motors. The outcomes of the predicted models indicate a noteworthy enhancement in meeting minimum performance requirements for both traction and steering motors.

This is an interesting article. A few comments:

1. In the introduction, some references for vehicle electrification/energy efficiency are outdated (not published within the past 2 years). The authors may consider citing some recently published papers such as: [1] 10.1016/j.est.2021.103749 [2] 10.1109/TTE.2022.3157652 [3] 10.1016/j.energy.2023.130097

2. In Figure 2, the reviewer questions whether the 3-DOF vehicle model are sufficient to capture vehicle dynamics since it is only planar and does not describe roll/pitch nor suspension dynamics.

3. All equations should followed by proper punctuation. This should be corrected. 

4. Some plots are not high quality (e.g., Figure 7 looks blurry). Please make sure to have all plots at least 300 dpi of resolution. 

5. The objective function of the optimization problem, along with the feasibility and solvability, needs to be clarified. 

6. The selections of neuron numbers in Figure 20 should also be clarified and justified. 

7. The conclusion section is too lengthy. Please make it more compact. 

Author Response

Dear Editor and Reviewer,

Please find attachment (Author's Reply to the Review Report (Reviewer 2)).

Best Regards

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript presents an intriguing paper on a motor model with new design parameters for traction and steering motors. The following are my comments:

(1) The manuscript uses artificial neural networks to predict the parameters of the motor, what is the complexity of this process? Can it meet the reliability of the vehicle in real-time operation?

(2) The degree of similarity between the dataset for artificial neural network training in the manuscript and the actual operational data should be explained.

(3) The generalizability of the method proposed in the manuscript should be analyzed; can the performance described in the manuscript still be met when the model of the applied vehicle is changed?

Author Response

Dear Editor and Reviewer,

Please find attachment (Author's Reply to the Review Report (Reviewer 3)).

Best Regards

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The reviewer would like to thank the authors for their detailed response and effort in revising the manuscript. The revised paper merits acceptance as is.