All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
I reviewed your detailed eight pages of comments in rebuttal. Thanks for your thorough job..
[# PeerJ Staff Note - this decision was reviewed and approved by Shawn Gomez, a PeerJ Section Editor covering this Section #]
**PeerJ Staff Note:** Although the Academic and Section Editors are happy to accept your article as being scientifically sound, a final check of the manuscript shows that it would benefit from further editing. Therefore, please identify necessary edits and address these while in proof stage.
For instance: In Figure 3 -> In the "data processing" box in that figure there is a "filp" operation shown and this should presumably be "flip".
Please make sure that you include a separate document explaining how you accounted for each of the comment made by referees for me to understand that the paper is ready to be accepted. I will review the final version, and the review rebuttal/explanation, before making my final decision.
Thanks for your interest in the journal.
**PeerJ Staff Note:** It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors agree that they are relevant and useful.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
In this paper, an enhanced CNN-LSTM attention model combining data augmentation, UGABO optimization, MSAWH loss function, and MPC correction is proposed for improving the accuracy of lithium-ion battery health state prediction, and experiments show that the method can significantly reduce the prediction error and improve the model robustness. The article is more comprehensive, but there is still some room for improvement in formatting, language accuracy, and logical unfolding.
1. Missing keywords.
2. The first line is not indented.
3. Some formulas are not centered.
4. Table content not vertically centered, some content not horizontally centered.
5. Although the importance of lithium-ion batteries and the challenges they face are mentioned, the background is slightly abbreviated for readers who are not familiar with the field. Specific issues of lithium-ion batteries in different application scenarios, such as the impact of battery performance degradation on range in electric vehicles and the possible safety risks associated with battery failures, could be described in more detail to enhance readers' understanding of the urgency of the research problem.
6. When describing the different research methods, it is mostly a separate introduction to each study, lacking a horizontal comparative analysis between them. A table or paragraph can be added to compare the advantages and disadvantages of existing methods in terms of algorithmic complexity, data requirements, prediction accuracy, etc., highlighting the relative advantages of the methods in this paper.
7. The labelling of information in the charts was not sufficiently clear and complete, and some charts lacked detailed legend descriptions or axis labels, which affected the understanding of the results.
8. It is suggested that a separate paragraph be added to discuss the practicality and limitations of the experimental results, such as the applicability of the model under different battery types or operating conditions, as well as the possible challenges (e.g., computational cost, real-time requirements, etc.) in practical applications, to provide a direction for the subsequent research.
9. The authors may discuss New computational methodology development based upon artificial intelligence (AI) for future materials and devices. Therefore, the introduction of recent progress of novel materials and novel devices may attract a broader readership. For example: DOI: 10.1016/j.vacuum.2025.114117; 10.1016/j.cplett.2025.141959;
10. The figure and table caption should be more informative, and I suggest the author add more tables.
11. What's the innovation of this article? The author should explain.
12. English should be improved.
In the experimental section, the analysis of the experimental curve is too brief, please conduct an in-depth analysis.
no comment
no comment
In short, in its current form, the paper is not suitable for acceptance. The paper needs rewriting by addressing the above-mentioned comments.
This paper proposes a method for battery SOH estimation based on CNN-LSTM. The architecture of the article is reasonable, and the experiments are very rich, so it is recommended to revise and accept it. There are several issues that need to be noted before acceptance:
1. The first contribution point can be removed because this dataset has been widely used. These contribution points can be reorganized.
2. It is recommended to add a description of the experimental platform.
3. Recently published literature will be taken into consideration, such as: A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery; A multiple aging factor interactive learning framework for lithium-ion battery state-of-health estimation.
4. The language can be further embellished by referring to relevant papers that have been accepted by PeerJ.
no comment
no comment
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.