A Novel Method for Battery SOC Estimation Based on Slime Mould Algorithm Optimizing Neural Network under the Condition of Low Battery SOC Value
Round 1
Reviewer 1 Report
1) Please explain clearly whether it is under low load conditions or when the SOC value of the battery is low, which will cause the estimated SOC value to increase. Judging from the contents of Tables 3, 4, 10, and 11, it seems to be the latter. If it is the latter, it is recommended that the author not use "at low power condition" to avoid misunderstanding.
2) There is a typo in the title of paragraph 2. Please fix it.
3) In Figure 25, the drawing of the decision symbol at the bottom is wrong. Please correct it.
4) Table 1 shows the battery specifications used in this research. The capacity is only 1.5Ah, which seems too small. Please explain whether the test results obtained are representative.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors of the paper title is “A novel method for Battery SOC Estimation under low power condition based on Slime Mould Algorithm optimizing neural network ” evaluated the possibility of using SMA neural network to enhance the SOC estimation especially at low power. The paper is well-written and ready to publish after a minor revision.
1- The introduction part is well-written and understandable. However, in a research paper it is not scientific to bring figures from previous studies and cite them. Please remove Fig. 1 to 3 and just explain them in the introduction part.
2- The number of figures are not right. The orders are not right. Please read the paper and revise accordingly.
3- Why didn’t the previous studies focus on the SOC prediction at low power? What information we can get from the SOC at low power for batteries? Please explain in the manuscript. What internal characteristics
4- Authors tested a new method in how many cells? What is the state of health of cell before testing?
5- Please provide the C rate for the discharging process. Is the C rate of discharging effect the modeling data?
6- Is the authors check the model in a different battery chemistry with higher power? Also it this method usable for a battery module?
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
The manuscript submitted focuses on developing a technique for estimating battery State of Charge (SOC) under low-power conditions. The subject matter aligns well with the journal's scope; however, several revisions are necessary for the paper to be considered for publication. My specific suggestions are as follows:
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Enhance the Abstract: The current abstract lacks any quantitative metrics. Please include significant results that you've obtained in your research to make it more informative.
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Revise Section 2.1: Language inconsistencies have been noticed starting from line 152. Kindly review and correct these minor errors.
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Optimize Figure Usage: The manuscript contains an excessive number of figures. Consider removing some of these and relocating them to the supplementary materials to streamline the paper's content.
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Include Comparative Analysis: It would strengthen your paper if you compared your method with various machine learning techniques to provide a comprehensive view of its effectiveness.
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Address Limitations and Future Work: Please elaborate on the limitations of your study and propose possible avenues for future research.
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Improve the Conclusion: The current conclusion fails to summarize key results from your study. Enhancing this section to include significant findings will make the conclusion more impactful.
Please see my comments above.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
The authors addressed all questions that l asked.
Author Response
Thank you for your efforts
