HybridoNet-Adapt: A domain-adapted framework for accurate lithium-ion battery RUL prediction

Khoa Tran; Bao Huynh; Tri Le; Lam Pham; Vy-Rin Nguyen; Duong Tran Anh; Hung-Cuong Trinh

doi:10.1371/journal.pone.0335066

Peer Review History

Original SubmissionAugust 7, 2025
10 Sep 2025 Decision Letter - Zhibin Zhao, Editor PONE-D-25-42933HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL PredictionPLOS ONE Dear Dr. Trinh, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Oct 25 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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There is no requirement to cite these works unless the editor has indicated otherwise. Additional Editor Comments : Reviewer #1: Overall, the manuscript presents an interesting framework (HybridoNet-Adapt) that combines domain adaptation with deep temporal feature extraction for lithium-ion battery RUL prediction. The idea of addressing domain shift through Maximum Mean Discrepancy (MMD)-based alignment and hybrid predictors is promising. However, several important issues remain that limit the clarity and rigor of the work. The following points should be addressed to further improve the manuscript: 1. On Page 3, the statement “Many existing methods predict RUL based on estimated maximum discharge capacity, representing the remaining life as a percentage of the nominal capacity” is actually the definition of SOH, not RUL. Please carefully distinguish between these two concepts. Accordingly, some references listed in Table 1 are not truly RUL prediction studies. 2. To further strengthen the introduction, it would be beneficial to include more recent and relevant works, such as DOI: 10.1016/j.jpowsour.2022.230975; 10.1016/j.est.2025.116024. 3. The manuscript currently lists five contributions. This number is excessive and somewhat redundant. It would be clearer and more impactful to condense and reorganize them into three key contributions. 4. Section titles should not contain references or colons. Please revise accordingly. 5. All equations should be numbered, and every variable used in the equations must be explained. Avoid using whole words as variable names in formulas. 6. In Figure 2, the arrows pointing from the MSE loss and MMD loss to the Input are confusing. Please check whether the statement is correct or explain the meaning of the arrows. 7. It appears that target-domain labels may have been used from Page 7. If so, why not directly train and test using the target-domain data? Please clarify whether target labels are available, and if they are, discuss the meaningfulness of the domain adaptation task under this setting. 8. In many published works using the TRI dataset, models are typically trained on Batch 1 and tested on Batches 2 and 3. If target-domain labels were used to aid model training, then the task defined in this paper may be easier, and the results may not provide strong evidence that the proposed method is more effective than existing approaches. 9. Hyperparameters should be presented in a table for clarity and reproducibility. Reviewer #2: This paper proposes HybridoNet-Adapt, a novel domain-adaptive framework for accurate Remaining Useful Life (RUL) prediction of lithium-ion batteries. Although the experimental results seem promising, there are still some major issues. Therefore, the paper needs to be rejected and resubmitted. Other comments: 1) The study lacks practical interpretability. Although the methodology is emphasized, the model predictions are not supported by physical explanations or visual analyses. 2) The baselines are mostly traditional methods or relatively simple deep learning models, while more recent approaches such as advanced transfer learning or self-supervised learning are not systematically compared. 3) While different feature extraction modules and alignment losses are examined, the ablation study remains incomplete, as it does not explore alternative feature selection strategies or the sensitivity to different sampling window lengths. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Overall, the manuscript presents an interesting framework (HybridoNet-Adapt) that combines domain adaptation with deep temporal feature extraction for lithium-ion battery RUL prediction. The idea of addressing domain shift through Maximum Mean Discrepancy (MMD)-based alignment and hybrid predictors is promising. However, several important issues remain that limit the clarity and rigor of the work. The following points should be addressed to further improve the manuscript: 1. On Page 3, the statement “Many existing methods predict RUL based on estimated maximum discharge capacity, representing the remaining life as a percentage of the nominal capacity” is actually the definition of SOH, not RUL. Please carefully distinguish between these two concepts. Accordingly, some references listed in Table 1 are not truly RUL prediction studies. 2. To further strengthen the introduction, it would be beneficial to include more recent and relevant works, such as DOI: 10.1016/j.jpowsour.2022.230975; 10.1016/j.est.2025.116024. 3. The manuscript currently lists five contributions. This number is excessive and somewhat redundant. It would be clearer and more impactful to condense and reorganize them into three key contributions. 4. Section titles should not contain references or colons. Please revise accordingly. 5. All equations should be numbered, and every variable used in the equations must be explained. Avoid using whole words as variable names in formulas. 6. In Figure 2, the arrows pointing from the MSE loss and MMD loss to the Input are confusing. Please check whether the statement is correct or explain the meaning of the arrows. 7. It appears that target-domain labels may have been used from Page 7. If so, why not directly train and test using the target-domain data? Please clarify whether target labels are available, and if they are, discuss the meaningfulness of the domain adaptation task under this setting. 8. In many published works using the TRI dataset, models are typically trained on Batch 1 and tested on Batches 2 and 3. If target-domain labels were used to aid model training, then the task defined in this paper may be easier, and the results may not provide strong evidence that the proposed method is more effective than existing approaches. 9. Hyperparameters should be presented in a table for clarity and reproducibility. Reviewer #2: This paper proposes HybridoNet-Adapt, a novel domain-adaptive framework for accurate Remaining Useful Life (RUL) prediction of lithium-ion batteries. Although the experimental results seem promising, there are still some major issues. Therefore, the paper needs to be rejected and resubmitted. Other comments: 1) The study lacks practical interpretability. Although the methodology is emphasized, the model predictions are not supported by physical explanations or visual analyses. 2) The baselines are mostly traditional methods or relatively simple deep learning models, while more recent approaches such as advanced transfer learning or self-supervised learning are not systematically compared. 3) While different feature extraction modules and alignment losses are examined, the ablation study remains incomplete, as it does not explore alternative feature selection strategies or the sensitivity to different sampling window lengths. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0335066.r001
Revision 1
22 Sep 2025 Author Response Response Letter We sincerely thank Editor-in-Chief, Associate Editor, and the reviewers for their constructive, pertinent, and useful comments on our manuscript. To Editor-in-Chief and Associate Editor We are pleased to see insightful and constructive comments from the reviewers. In this resubmission, we have attempted to address the reviewers’ concerns and revised the manuscript accordingly. Further details about all the changes we made can be found in our point-to-point response to the reviewers’ comments below. In the revised manuscript, we marked the changes in yellow. To Reviewer 1 On Page 3, the statement "Many existing methods predict RUL based on estimated maximum discharge capacity, representing the remaining life as a percentage of the nominal capacity" is actually the definition of SOH, not RUL. Please carefully distinguish between these two concepts. Accordingly, some references listed in Table 1 are not truly RUL prediction studies. Response: Thank you for your comment. We have revised the statement to reflect the RUL concept. The updated text begins with "Historical data-independent methods estimate the future capacity trajectory ..." on Page 3 and "RUL can be assessed based on the number of remaining cycles" on Page 2. In addition, we have removed a reference that predicts cycle life rather than RUL from Table 1. Comment 2: To further strengthen the introduction, it would be beneficial to include more recent and relevant works, such as DOI: 10.1016/j.jpowsour.2022.230975; 10.1016/j.est.2025.116024. Response: Thank you for your comment. We have added the two references by updating Table 1 and including a paragraph beginning with "[31] proposed a transferable RUL prediction ..." in the Problem Statement and Potential section. Comment 3: The manuscript currently lists five contributions. This number is excessive and somewhat redundant. It would be clearer and more impactful to condense and reorganize them into three key contributions. Response: Thank you for your comment. We have updated the contributions and condensed them into three key points. The revised text begins with “We propose a historical data-independent RUL prediction framework for . . . ”. Comment 4: Section titles should not contain references or colons. Please revise accordingly. 1 Response: Thank you for your comment. We have remove the references and colons from two section titles (LSTM, Multihead Attention, NODE). Comment 5: All equations should be numbered, and every variable used in the equations must be explained. Avoid using whole words as variable names in formulas. Response: Thank for your comment. We have revised the equations in the Preliminaries section, ensuring that all equations are numbered and every variable is clearly defined, while avoiding the use of whole words as variable names. Comment 6: In Figure 2, the arrows pointing from the MSE loss and MMD loss to the Input are confusing. Please check whether the statement is correct or explain the meaning of the arrows. Response: Thank for your comment. We have revised Figure 2 by removing the arrows from the MSE and MMD loss blocks to the input, ensuring that the diagram accurately reflects the intended training flow. Comment 7: It appears that target-domain labels may have been used from Page 7. If so, why not directly train and test using the target-domain data? Please clarify whether target labels are available, and if they are, discuss the meaningfulness of the domain adaptation task under this setting. Response: Thank you for your comment. Yes, target-domain labels are available. To address this, we created HybridoNet (without domain adaptation), which consists of only a feature extractor and a target predictor and is trained solely on labeled target data. The updated text, beginning with "For validating the proposed domain adaptation used in HybridoNet-Adapt, we ...", has been added to the Proposed method / HybridoNet-Adapt: A Proposed RUL Prediction model with Novel Domain Adaptation section. Comment 8: In many published works using the TRI dataset, models are typically trained on Batch 1 and tested on Batches 2 and 3. If target-domain labels were used to aid model training, then the task defined in this paper may be easier, and the results may not provide strong evidence that the proposed method is more effective than existing approaches. Response: Thank you for your comment. We have added Figure 11(a), which compares our proposed HybridoNet-Adapt (146.52 RMSE) with the MSFEH 2025 state-of-the-art model (161.81 RMSE). This result shows that our proposed method, even when using target-domain labels, substantially outperforms the current state-of-the-art under this setting. For the second dataset, we have removed the comparison with the Dual-input DNN because it 2 was conducted under a different experimental setup. Our proposed method follows the official split of the second dataset, with 55 LIBs for training and 22 LIBs for testing. In contrast, the Dual-input DNN work re-split the dataset, using 70 batteries for training and only 7 for testing, which makes its evaluation less generalizable. Comment 9: Hyperparameters should be presented in a table for clarity and reproducibility. Response: Thank you for your comment. We have included Table 2 for the hyperparameters, along with a paragraph explaining the notation, beginning with "The hyperparameters of the proposed HybridoNet-Adapt are summarized ...". To Reviewer 2 Comment 1: The study lacks practical interpretability. Although the methodology is emphasized, the model predictions are not supported by physical explanations or visual analyses. Response: Thank you for your comment. We acknowledge the importance of interpretability in bridging the gap between predictive performance and physical insights. We plan to integrate Physics-Informed Neural Networks in future work, as noted in the added text beginning with “In future work, we plan to explore Physics-Informed Neural Networks (PINNs) . . . ” in the Experiments and discussion Section. Comment 2: The baselines are mostly traditional methods or relatively simple deep learning models, while more recent approaches such as advanced transfer learning or self-supervised learning are not systematically compared. Response: Thank you for your comment. We have compared our model with recent state-of-theart approaches. For the first dataset, we have added Figure 11(a), which compares our proposed HybridoNet-Adapt (146.52 RMSE) with the MSFEH 2025 state-of-the-art model (161.81 RMSE). This result shows that our proposed method substantially outperforms the current state-of-theart under this setting. For the second dataset, we have added the Structural Pruning 2024 state-of-the-art model in Figure 11(b). We also removed the comparison with the Dual-input DNN because it was conducted under a different experimental setup. Our proposed method follows the official split of the second dataset, with 55 LIBs for training and 22 LIBs for testing. In contrast, the Dual-input DNN re-split the dataset, using 70 batteries for training and only 7 for testing, which makes its evaluation less generalizable. Comment 3: While different feature extraction modules and alignment losses are examined, the ablation study remains incomplete, as it does not explore alternative feature selection strategies or the sensitivity to different sampling window lengths. Response: Thank you for your comment. We have added an ablation study (see Figure 5 and the accompanying text beginning with "Figure 5a presents the feature importance ranking derived 3 from XGBoost...") to compare our chosen statistical feature extraction method with alternative approaches. 4 Attachments Attachment Submitted filename: Response_to_Reviewers.pdf https://doi.org/10.1371/journal.pone.0335066.r002
6 Oct 2025 Decision Letter - Zhibin Zhao, Editor HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction PONE-D-25-42933R1 Dear Dr. Trinh, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors have provided satisfactory and detailed responses to all of my previous comments, and I have no further questions or concerns at this stage. Reviewer #2: I have no further questions, and the manuscript can be considered for acceptance.................... ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0335066.r003
Formally Accepted
Acceptance Letter - Zhibin Zhao, Editor PONE-D-25-42933R1 PLOS ONE Dear Dr. Trinh, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Zhibin Zhao Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0335066.r004

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