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The paper may be accepted.
[# PeerJ Staff Note - this decision was reviewed and approved by Mehmet Cunkas, a PeerJ Section Editor covering this Section #]
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The contribution is not properly justified by experimental findings.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff 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.
Although the authors have revised their manuscript, the presentation of this work still needs improvement. The central purpose of the investigation here is not evident in terms of novelty and presented advantages, which align with current literature.
It should be demonstrated by sufficient experimental tests.
It has not been proven that the method is useful for complex working conditions. Please try to show your innovation clearly.
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The authors have addressed the comments of the reviewers.
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1. The novelty of the work should be explained in detail in the Abstract and Conclusion. The expression of the abstract should be improved. A more detailed presentation of innovation should be conducted.
2. Please pay more attention to the expression check. Please try to conduct more literature reviews. Please provide a brief presentation of your proposed method.
3. Please highlight the part improved by your research and try to make it more obvious.
4. More recent literature should be cited and analyzed for mathematical analysis, such as Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation, Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries, and Improved multiple feature-electrochemical thermal circuitry modeling of lithium-ion batteries at low temperature with real-time coefficient correction.
5. The writing of the paper should be improved. Please try to improve all the content. The presentation should be revised to clarify the reader's understanding of your idea, making the logic more precise and accurate. Additionally, the innovation and realization should be described in clear terms.
6. The mathematical analysis should be presented more clearly with figures and equations to convey your ideas effectively.
7. Please express your innovation briefly in the conclusion.
8. A comparison with references should be conducted for verification of the advantage.
9. Many contents are expressed using the traditional methods for this version. Please pay more attention to your content.
10. The structure can be improved. As other researchers are more concerned with your idea and innovation, the expression of your method can be conducted with a more detailed expression.
In this work, a hybrid method comprising a random forest (RF) and a long short-term memory (LSTM) is proposed for the lifetime prediction of batteries. Validations on experimental datasets confirm the effectiveness of the proposed method. Overall, this work is of interest to the readers in the field of battery health monitoring, and the authors are encouraged to address the following issues:
1. Battery health monitoring is a hot topic, and the authors are encouraged to clarify the difference between this work and existing machine learning applications in this area, such as “Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning”, “Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems”.
2. As the LSTM model is generally implemented in an end-to-end manner, why is it necessary to conduct a feature selection with an RF?
3. From Fig.6, it seems the proposed method does not show an evident improvement compared with other benchmarks.
4. Also in Figure 6, the order of metrics is not consistent for different algorithms.
5. Since battery degradation is subject to factors such as materials and operating conditions, the authors are encouraged to validate the model on different datasets. Besides, independent runs are suggested to rule out random factors.
More validations on other datasets are encouraged.
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