Fuzzy Logic-Based Energy Management Strategy for Hybrid Renewable System with Dual Storage Dedicated to Railway Application

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Summary: This paper presents a hybrid renewable energy station for urban railway systems, integrating photovoltaics, wind, batteries, and supercapacitors. A fuzzy-based energy management strategy (F-EMS) is proposed to coordinate power flow and ensure stable DC bus voltage. Simulations in MATLAB/Simulink confirm the strategy's effectiveness under varying energy production and train demand scenarios.

General comment. This study offers a promising approach to enhancing urban rail sustainability through hybrid renewable energy integration and intelligent energy management.

Comments for improvement.

1. The introduction lacks a clear and coherent structure. I recommend reorganizing it using the following elements: general context, motivation, literature review, main contributions and scope, and paper organization. Additionally, the literature review should be strengthened by including a comparative table that summarizes existing approaches, helping to better highlight the novelty and contribution of the present work.
2. Remove bold style at the end of the introduction.
3. The mathematical modeling presented in the paper is solid. However, the authors should provide a clearer justification for the adoption of fuzzy theory over alternative control or energy management methodologies, highlighting its specific advantages in this context.
4. Numerical validations are adequate and confirm the effectiveness of the proposed EMS system. However, the authors must improve the section regarding conclusions with some numerical data.
5. The primary limitation of this study lies in the absence of a comparative analysis with existing control or energy management methods. The authors are encouraged to justify this omission and explicitly acknowledge it in the section outlining the contributions and scope of the work. Including a comparison or discussion of related methods would strengthen the validity and relevance of the proposed approach.

Major revisions are requested. 

 

Author Response

Response to Reviewer 1

We would like to express our sincere gratitude to the esteemed reviewer for the valuable and constructive feedback provided. Your insightful comments have significantly contributed to improving the overall quality, clarity, and rigor of our manuscript. In response, we have carefully revised the manuscript to incorporate all the suggested modifications. For ease of review, all changes have been highlighted in red in the revised version.

Below, we provide detailed responses to each of your comments and suggestions. Each point has been thoroughly addressed, and the corresponding revisions have been implemented to further strengthen the manuscript.

General Comment and Summary:

Summary: This paper presents a hybrid renewable energy station for urban railway systems, integrating photovoltaics, wind, batteries, and supercapacitors. A fuzzy-based energy management strategy (F-EMS) is proposed to coordinate power flow and ensure stable DC bus voltage. Simulations in MATLAB/Simulink confirm the strategy's effectiveness under varying energy production and train demand scenarios.

General comment. This study offers a promising approach to enhancing urban rail sustainability through hybrid renewable energy integration and intelligent energy management.

Response to Reviewer 1 – General Comment and Summary:

We sincerely thank the reviewer for the positive and encouraging evaluation of our work. We appreciate your recognition of the relevance and potential of our proposed approach in promoting sustainable energy integration within urban railway systems. Your summary accurately reflects the core objectives and contributions of the study.

Comments 1:

The introduction lacks a clear and coherent structure. I recommend reorganizing it using the following elements: general context, motivation, literature review, main contributions and scope, and paper organization. Additionally, the literature review should be strengthened by including a comparative table that summarizes existing approaches, helping to better highlight the novelty and contribution of the present work.

Response 1:

We appreciate the reviewer's thoughtful and helpful suggestion.  As a result, the Introduction section has been completely rewritten to enhance its organization and readability.  The updated version now progresses logically and comprises:

1.1. General Context and motivation,  

1.2. Literature review,

1.3. Contributions and scope.

We have also included a new comparison table (Table 1) to the literature review section as advised.  Table presents a comparative review of the most prominent EMS techniques. It highlights their key contributions, advantages, and limitations. Please see the updated Introduction in the revised manuscript.

Comments 2:

Remove bold style at the end of the introduction.

Response 2:

Thanks to the reviewer's comment, the bold style at the end of the introduction has been removed.

Comments 3:

The mathematical modeling presented in the paper is solid. However, the authors should provide a clearer justification for the adoption of fuzzy theory over alternative control or energy management methodologies, highlighting its specific advantages in this context.

Response 3:

We thank the reviewer for this valuable observation. an energy management strategy (EMS) is required in a DC microgrid with a hybrid energy storage system (HESS). A fuzzy energy management strategy (F_EMS) establishes the choice of variables in advance and builds a library of fuzzy rules. The controller inputs are transformed into control outputs for each part of the HESS in the DC microgrid station by two processes of fuzzy inference and defuzzification. The relative simplicity of fuzzy rule controllers enables tweaking and adaptation when required, increasing the degree of control. Because of its nonlinear structure, it is even more beneficial in complex models.

In our application the F-EMS is established effectively coordinate the BTs and SCs’ charging and discharging process; it ensures power balance and voltage stability.

According to Figure 8, the first fuzzy logic controller is used for power flow control; it determines the logical states of the switches (S1 and S2) used for the selection of the SCs and BTs current. The second fuzzy logic controller is employed to choose the exact reference current for BTs and SCs by keeping each storage system's state of charge (SOC) within reasonable bounds, so protecting them from overcharging and excessive discharge. This strategy is intended to govern the power flow from the BTs and SCs.

Another fuzzy logic controller is used in this paper to track the maximum power of the photovoltaic array, providing fast and accurate tracking regardless of atmospheric variations (irradiation and temperature) compared to conventional methods.

Comments 4:

Numerical validations are adequate and confirm the effectiveness of the proposed EMS system. However, the authors must improve the section regarding conclusions with some numerical data.

Response 4:

We thank the reviewer for this valuable and constructive recommendation. We agree that including specific numerical results in the Conclusion section would enhance the clarity and impact of our findings. In response, we have revised the Conclusion to incorporate representative numerical data from the simulation results, such as the SOC ranges for the battery and supercapacitors.

Comments 5:

The primary limitation of this study lies in the absence of a comparative analysis with existing control or energy management methods. The authors are encouraged to justify this omission and explicitly acknowledge it in the section outlining the contributions and scope of the work. Including a comparison or discussion of related methods would strengthen the validity and relevance of the proposed approach.

Response 5:

We express our heartfelt gratitude to the reviewer for this significant observation.  We recognize that the lack of a comparison analysis with other established energy management techniques (such as RBC, DBC, FBC, and AI-based approaches) ca limitation in the current study.  This decision was primarily made to keep the scope focused on the development, implementation, and initial validation of the proposed fuzzy-based EMS (F-EMS) within the three studied scenarios.

Importantly, we would like to underline that a follow-up study is now in progress, which will give a full comparison analysis between our F-EMS and other traditional and intelligent EMS strategies.  This planned work is designed as a direct continuation of the current research and will be submitted for publication in the near future. We are convinced that this continuation will give a more thorough knowledge of the benefits and trade-offs of the F-EMS in compared to competing techniques. Furthermore, the comparative analysis with other energy management methods has been explicitly included as a future research perspective in the Conclusion section of the revised manuscript.

 

Reviewer 2 Report

Comments and Suggestions for Authors

The abstract and introduction of this article, which addresses an interesting and significant topic, are very well written. However, it is recommended that the authors elaborate at the end of the introduction on the specific contributions of the study to the existing literature, clearly situating their work within the scholarly discourse.

The second section appears somewhat brief. While it is acknowledged that the literature on railway transportation may be limited, a broader discussion could be incorporated by exploring the connection between electric vehicles (EVs), which form an integral part of renewable energy systems (RES), and railway transportation. Relevant studies on EVs that could enrich this discussion include https://doi.org/10.1016/j.sftr.2025.100720 and https://doi.org/10.1016/j.epsr.2024.110612.

The methodological section adequately describes the models employed. Nevertheless, it would strengthen the paper if the authors briefly justified, in a single sentence, why these specific models were chosen.

The results section is well presented. However, following the discussion of the recommended policy implications and the study’s limitations, it would be valuable to include suggestions for future research directions.

 

Author Response

Response to Reviewer 2

We would like to express our sincere gratitude to the esteemed reviewer for the valuable and constructive feedback provided. Your insightful comments have played a significant role in enhancing the overall quality, clarity, and rigor of our manuscript. In response, we have carefully revised the manuscript to incorporate all the suggested modifications. For ease of review, the changes have been highlighted in red within the revised version.

Below, we present detailed responses to each of your comments and suggestions. Each point has been addressed thoroughly, and the corresponding revisions have been implemented accordingly to strengthen the manuscript.

Comments 1:

The abstract and introduction of this article, which addresses an interesting and significant topic, are very well written. However, it is recommended that the authors elaborate at the end of the introduction on the specific contributions of the study to the existing literature, clearly situating their work within the scholarly discourse.

Response 1:

We thank the reviewer for the positive feedback regarding the quality of the abstract and introduction. We fully agree with the suggestion to explicitly articulate the specific contributions of our work in relation to existing studies. Accordingly, we have revised the final paragraph of the Introduction to highlight the novel aspects and contributions of our proposed F-EMS approach and to better situate our research within the existing scholarly literature. This revision clarifies how our work advances the state of the art in energy management for rail-based microgrids with hybrid storage systems. Please refer to the revised Introduction section for further details. The revised text now reads as follows (end of the Introduction section):

“In this paper, a new energy management strategy based on fuzzy logic (F-EMS) is proposed, specifically designed to handle the nonlinear and uncertain behaviors associated with renewable energy sources. By combining the advantages of fuzzy logic with the use of a low-pass filter, this method directs rapid power fluctuations toward the supercapacitors. It thus enables dynamic balancing of the energy flow within the HESS, while respecting the charging constraints of both batteries and supercapacitors, and maintaining a stable DC bus voltage.

By contrast, classical energy management techniques such as RBC, DBC, and FBC exhibit a great deal of rigidity. Their usefulness in railway applications integrating renewable energy sources is limited by their general sensitivity to system parameter variations and lack of adaptability to changing or disturbed conditions. Furthermore, although artificial intelligence-based methods (ANNs, and EAs) perform well in terms of adaptation and optimization, they frequently require large amounts of training data and significant processing capacity. These limitations make it difficult to deploy them in real-time, especially in decentralized or embedded systems like those found in smart rail networks. F-EMS is therefore a valuable trade-off between robustness, performance, and complexity—particularly well-suited for rail microgrid energy management.

The main contributions of this study are:

• The novelty of this article concerns the fuzzy logic-based EMS (F-EMS) proposed for a railway system integrating RES and HESS.
• The study introduces a new F-EMS strategy for DC bus voltage control in a microgrid station under varying irradiance and wind conditions.
• The fuzzy logic controller, associated with a low-pass filter, is utilized for storage device energy management, taking into account the constraints imposed by BTs and SCs, charging within acceptable limits.
• The proposed F-EMS performance is tested and validated under variable irradiance and wind speeds for three stations (A, B, and C) where trains are equipped with onboard SCs for rapid charging during stops.
• Maximum power point tracking algorithms are implemented for photovoltaic and wind power optimization.
• The study illustrates the simplicity and robustness of F-EMS and demonstrates the potential of intelligent fuzzy logic techniques for energy management, opening up new avenues for traction system research.”

Comments 2:

The second section appears somewhat brief. While it is acknowledged that the literature on railway transportation may be limited, a broader discussion could be incorporated by exploring the connection between electric vehicles (EVs), which form an integral part of renewable energy systems (RES), and railway transportation. Relevant studies on EVs that could enrich this discussion include https://doi.org/10.1016/j.sftr.2025.100720 and https://doi.org/10.1016/j.epsr.2024.110612.

Response 2:

We thank the reviewer for the valuable and constructive suggestion. Accordingly, the two recommended references [43] and [44] have been added to the revised manuscript, along with a descriptive paragraph that discusses their relevance to the railway energy management context. This new content appears in the second paragraph of the ‘’Studied Railway System’’ section.

Comments 3:

The methodological section adequately describes the models employed. Nevertheless, it would strengthen the paper if the authors briefly justified, in a single sentence, why these specific models were chosen.

Response 3:

We thank the reviewer for this helpful remark. In response, a brief justification for the choice of models have been added at the end of the “Energy management strategy section”. Please refer to the revised manuscript for the updated text.

We thank the reviewer for this helpful remark. In response, we have added a brief justification for the choice of models at the end of the Energy Management Strategy section. This addition clarifies the rationale behind selecting the specific models used in the study.

Comments 4:

The results section is well presented. However, following the discussion of the recommended policy implications and the study’s limitations, it would be valuable to include suggestions for future research directions.

Response 4:

We thank the reviewer for this constructive suggestion. In response, we have added a dedicated paragraph outlining potential directions for future research following the conclusion of the article. These suggestions aim to extend the scope of the current study and address its limitations, while also opening new avenues for practical applications and comparative analysis. Please refer to the updated ‘’Conclusion’’ section in the revised manuscript.

 

 

Reviewer 3 Report

Comments and Suggestions for Authors

The paper addresses an important and timely topic in railway energy management by proposing the F-EMS approach for managing hybrid energy systems. While the concept is promising, there are several critical aspects that require further clarification and elaboration:

1. The simulation outcomes are not validated against real-world data. To enhance credibility, it is recommended to apply the model to a real-world scenario where empirical data can be used to verify the simulated behavior—particularly for the State of Charge (SOC). Even if the renewable and battery components are not yet implemented, an SOC validation would still be valuable.

2. The contribution of renewable energy sources (e.g., PV and wind) in the simulation is not clearly quantified. Furthermore, it would be beneficial to provide a more detailed description of the hybrid energy system, including where the PV panels and wind turbines are located (e.g., at stations or onboard trains), and the logistics of the charging process—specifically the location and duration of charging events.

3. Figures 11, 12, and 13 present wind power curves with noticeably different frequencies—low at Station A and significantly higher at Station C. An explanation for these variations is necessary to understand the modeling assumptions and their implications.

4. The outcomes for the three stations and two trains are presented individually but not collectively analyzed. A summary or comparative discussion at the end would help readers assess whether the F-EMS approach demonstrates effectiveness across different scenarios.

5. The SOC curve for Train 2 (operating from Station C to Station A) appears to be missing. Including this would provide a more complete picture of the system's performance.

6. The generalizability of the proposed model and its results is not addressed. The current study simulates only a one-hour window, which overlooks longer-term fluctuations in renewable generation (e.g., seasonal variations). It would be useful to discuss how the model would perform with more trains, additional stations, or over extended periods.

7. The symbols used in Equations (1–8) are not defined within the main text, making it hard for readers to cross-reference the appendix repeatedly. It is advisable to define key symbols directly where the equations are introduced.

Author Response

Response to Reviewer 3

We sincerely thank the reviewer for their thoughtful and constructive feedback. Your comments have been instrumental in helping us improve the clarity, coherence, and overall quality of our manuscript.

In response, we have revised the manuscript accordingly, with all changes clearly marked in red for ease of reference. Below, we address each of your comments in detail and explain the corresponding revisions made throughout the paper.

General Comment and Summary:

The paper addresses an important and timely topic in railway energy management by proposing the F-EMS approach for managing hybrid energy systems.

Response to Reviewer 1 – General Comment and Summary:

We thank the reviewer for recognizing the relevance and potential of our proposed F-EMS approach in the context of railway energy management.

Comments 1:

The simulation outcomes are not validated against real-world data. To enhance credibility, it is recommended to apply the model to a real-world scenario where empirical data can be used to verify the simulated behavior—particularly for the State of Charge (SOC). Even if the renewable and battery components are not yet implemented, an SOC validation would still be valuable.

Response 1:

We applaud the reviewer for this crucial and interesting comment.  We absolutely agree that testing the simulation outcomes—particularly the State of Charge (SOC) behavior—against real-world data will considerably increase the credibility and practical applicability of the proposed F-EMS. It should be highlighted that this work focuses on a simulation-based validation under actual operating settings.  Also, we would like to underline that an other study is currently ongoing where we will apply the suggested model to a real-world situation across extended simulation periods, encompassing seasonal fluctuations and a larger-scale system with several trains and stops.  In this expanded research, we intend to add empirical SOC data—where available—for model calibration and validation reasons.  The findings of this enlarged inquiry will be submitted for publication in the near future.  We feel this strategy not only satisfies the reviewer’s proposal but also increases the adaptability and resilience of the F-EMS for implementation in real-world railway microgrids.  Furthermore, this aspect has been addressed as a potential study path in the Conclusion section of the updated text.

Comments 2:

The contribution of renewable energy sources (e.g., PV and wind) in the simulation is not clearly quantified. Furthermore, it would be beneficial to provide a more detailed description of the hybrid energy system, including where the PV panels and wind turbines are located (e.g., at stations or onboard trains), and the logistics of the charging process—specifically the location and duration of charging events.

Response 2:

We thank the reviewer for this valuable question. In the studied system, the renewable energy sources—namely photovoltaic panels and wind turbines—are installed at fixed locations within each station, with each source rated at 3 MW. The energy generated by these systems is injected into a shared DC bus and coupled with hybrid storage elements to ensure the continuity and stability of the power supply.

The hybrid energy storage system comprises batteries for mid- and long-term energy storage, and supercapacitors for rapid, short-term response. The supercapacitors are particularly effective in managing sudden power fluctuations, thereby enhancing the dynamic stability of the system. This combination allows the system to effectively mitigate the intermittency of renewable energy sources while ensuring a stable and reliable power supply to meet the operational demands of the railway system.

Each station serves two electric trains, following predefined charging scenarios. When a train arrives at a station, it connects to the local microgrid via a raised pantograph. A constant charging current of 900 A is then delivered over a 5-minute window, simulating realistic fast-charging operations.

Comments 3:

Figures 11, 12, and 13 present wind power curves with noticeably different frequencies—low at Station A and significantly higher at Station C. An explanation for these variations is necessary to understand the modeling assumptions and their implications.

Response 3:

We thank the reviewer for this valuable observation. The reasons for these differences may lie in the different wind speed profiles used for each station in order to test the robustness of the F-EMS system to environmental conditions. A stable wind profile was considered for station A. Station C, on the other hand, was exposed to a highly variable wind profile with high-frequency components, simulating unstable weather conditions.

This decision was carefully considered and is part of the methodology aimed at evaluating the adaptive behavior of the energy management system in various contexts of renewable energy variations. The aim is to demonstrate that even under high dynamic loads, such as those observed at Station C, the F-EMS approach can maintain the stability of the DC bus and the regulation of the Batt/SC state of charge.

Comments 4:

The outcomes for the three stations and two trains are presented individually but not collectively analyzed. A summary or comparative discussion at the end would help readers assess whether the F-EMS approach demonstrates effectiveness across different scenarios.

Response 4:

Thanks to the reviewer for this important remark. In this paper, the obtained results for the three stations and the two trains have been presented separately in order to highlight the operational specificities of each station. However, a global and comparative view is necessary to better evaluate the effectiveness and robustness of the energy management approach based on the F-EMS. In response, we have added a new subsection in the revised manuscript titled “Comparative Performance Analysis of F-EMS for the Three Charging Stations.”. Please see the revised version.

Comments 5:

Comment 5:  The SOC curve for Train 2 (operating from Station C to Station A) appears to be missing. Including this would provide a more complete picture of the system's performance.

Response 5:

We thank the reviewer for pointing out this oversight. You are absolutely right—it was an omission in the original version. In response, the simulation results for Train 2 traveling from Station C to Station A have now been included in the revised manuscript (please see the Figure 15).

Comments 6:

The generalizability of the proposed model and its results is not addressed. The current study simulates only a one-hour window, which overlooks longer-term fluctuations in renewable generation (e.g., seasonal variations). It would be useful to discuss how the model would perform with more trains, additional stations, or over extended periods.

 

 

Response 6:

Many thanks to the reviewer for this question. The complementarity between PV and wind generation, associated with the adaptability of the F-EMS, is an important factor in guaranteeing a reliable and efficient power supply, even in critical load situations such as train recharging.

The decision to limit the simulation to a time window of one hour is based on methodological considerations linked to the objective of this study, which is to analyze the instantaneous and dynamic behavior of the hybrid system under typical operating conditions. The studied system combines complementary renewable sources, namely photovoltaics and wind power, whose production profiles can, in the short term, complement each other. This complementarity makes it possible to partially smooth out production fluctuations and guarantee a more stable supply over longer periods.

In addition, the one-hour window has been chosen to allow detailed analysis of the system's behavior, particularly with regard to changes in the state of charge of the battery and the supercapacitor, power management, and interactions between the various sources. The aim is to assess the system's performance in a representative situation before considering an extension to longer-term simulations incorporating seasonal variations and a larger number of trains or stations.

Comment 7: 

The symbols used in Equations (1–8) are not defined within the main text, making it hard for readers to cross-reference the appendix repeatedly. It is advisable to define key symbols directly where the equations are introduced.

Response 7:

Many thanks for the reviewer's request; the symbols used in mentioned equations have been clearly defined within the text in the revised paper.

 

English Language Revision: 
This manuscript has been edited for grammar, clarity, and academic tone by a native English speaker.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors. I appreciate your effort in improving your manuscript. This is now accepted as it is. Good job.

Reviewer 3 Report

Comments and Suggestions for Authors

All comments and suggestions are now addressed and explained, thanks to the authors.