Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript presents a well-structured and technically sound study on the application of a fuzzy logic-based PID controller for optimizing electric vehicle (EV) battery charging under photovoltaic (PV) power generation with inherent uncertainties. The research is relevant, timely, and addresses a clear gap in renewable-integrated EV charging systems. The simulation-based methodology is appropriate, and the results demonstrate meaningful improvements over conventional control strategies. However, there are a few places to be clarified:

 

1) While the fuzzy-PID is compared broadly with “conventional PID,” no quantitative results (e.g., table or plots) are provided for a fixed-gain PID under the same test conditions. The direct comparative analysis should be added.

2) There is limited discussion on Battery Dynamics, but only focuses on voltage regulation and power quality. The battery state-of-charge (SoC) behavior, temperature effects, or long-term degradation under the proposed control strategy have not analyzed.

3) The work is entirely simulation-based. While MATLAB/Simulink is a powerful tool, hardware-in-the-loop (HIL) or prototype validation would be necessary before real-world deployment. The authors mention this as future work—it should be emphasized as a critical next step.

4)The performance is validated under a specific set of irradiance and load profiles. Testing under more diverse and extreme real-world scenarios (e.g., partial shading, grid faults, multi-EV charging) would better demonstrate robustness.

5) The organization of the paper should be reconsidered. Section 4 is missing.

6) There are a lot of format problem. For example, after the equation, ‘where:’, it is completely not appropriate.

7) Section 5, Conclusion is too long.

8) Table 4, what does it mean? Not clear.

9) Fig. 3.1 and Fig. 3.2, they are not meaningful.

10) Fig. 3.1, lack of meaning.

12) No comparison experiments and no quantitative results. Please add more.

Comments on the Quality of English Language

Can be imprvoed, especailly the format. 

Author Response

1) While the fuzzy-PID is compared broadly with “conventional PID,” no quantitative results (e.g., table or plots) are provided for a fixed-gain PID under the same test conditions. The direct comparative analysis should be added.

Author Responses (1):

• A new subsection (After Section 3.3) titled “Comparative Evaluation with Fixed-Gain PID Controller” has been added as section 3.4
• A new table summarizing quantitative metrics are included.

These additions clearly demonstrate the superior performance of the fuzzy-PID approach under identical operating conditions.

2) There is limited discussion on Battery Dynamics, but only focuses on voltage regulation and power quality. The battery state-of-charge (SoC) behavior, temperature effects, or long-term degradation under the proposed control strategy have not analyzed.

Author Responses (2):

We appreciate this important observation. A new paragraph (Section 3.5) has been added in the Result and Discussion section elaborating how the proposed controller influences:

• SoC trajectory during charging
• Battery thermal considerations
• Charging-induced degradation mechanisms

While these aspects were not explicitly simulated in this study, we now discuss their expected behavior and cite supporting literature. We also clarify that full SoC-thermal-aging modelling will be included in our future HIL-based research.

3) The work is entirely simulation-based. While MATLAB/Simulink is a powerful tool, hardware-in-the-loop (HIL) or prototype validation would be necessary before real-world deployment. The authors mention this as future work—it should be emphasized as a critical next step.

Author Responses (3):

We appreciate this point. We have added to section 3.5 last paragraph to clearly emphasize that:

• This study is purely simulation-based;
• Hardware-in-the-loop (HIL) and prototype development represent essential next steps;
• The proposed fuzzy-PID structure is lightweight and suitable for DSP/ARM implementation.

4) The performance is validated under a specific set of irradiance and load profiles. Testing under more diverse and extreme real-world scenarios (e.g., partial shading, grid faults, multi-EV charging) would better demonstrate robustness.

Author Responses (4):

We thank the reviewer for this suggestion. We agree that testing under diverse scenarios is important to demonstrate controller robustness. Accordingly, we have added a new paragraph in the Results and Discussion section explaining the expected controller behavior under:

• Partial shading conditions,
• Sudden grid-side disturbances, and
• Multi-EV charging scenarios.

5) The organization of the paper should be reconsidered. Section 4 is missing.

Author Responses:

We thank the reviewer for pointing out the organization issue. In the original manuscript, Section 4 was dedicated to Discussion. In our revised manuscript, the Discussion content has been merged with the Results section and renamed “Results and Discussion.” Consequently, the Conclusion is now presented as Section 4. All section numbers throughout the manuscript have been updated to ensure consistency.

6) There are a lot of format problem. For example, after the equation, ‘where:’, it is completely not appropriate.

Author Responses:

All equations throughout the manuscript have been carefully reformatted to meet MDPI style guidelines:

“where:” has been corrected to “where,”

7) Section 5, Conclusion is too long.

Author Responses:

The Conclusion section has been significantly shortened.

8) Table 4, what does it mean? Not clear.

Author Responses:

Table 4. (revised table 6) Summary of Key Results and Discussion Points of the Proposed Fuzzy-PID Controlled EV Charging System

9) Fig. 3.1 and Fig. 3.2, they are not meaningful.

Author Responses:

Thank you for these comments. Figures 3.1 and 3.2 (revised fig 4) meaning have been revised to improve clarity and scientific meaning.

10) Fig. 3.1, lack of meaning.

Author Responses:

Thank you for these comments. Figures 3.1 (revised fig 4) meaning have been revised to improve clarity and scientific meaning.

12) No comparison experiments and no quantitative results. Please add more.

Author Responses:

Additional comparative simulations and quantitative evaluations have now been included in table 3.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript introduces a relevant and technically solid fuzzy-PID control strategy for photovoltaic (PV)-based EV charging systems.

The simulation approach is thorough and clearly presented, with well-defined metrics and consistent results. The adaptive switching frequency control is a valuable contribution that enhances system efficiency and reliability. However, the study overstates its readiness for real-world deployment, as no hardware validation or prototype testing is presented.

Please consider tempering such claims in the abstract and conclusion. Additionally, the manuscript includes a relatively high number of self-citations, which should in my opinion be reduced or more clearly justified.

Finally, some figures would also benefit from improved label clarity. Minor inconsistencies in mathematical notation (e.g., subscripts and variable names) should be addressed for readability.

Overall it's a good work, I think a minor revision would do the trick to get this manuscript to a publishable state.

Comments on the Quality of English Language

The English is generally clear but can be improved for precision and fluency. Some sentences are overly long or contain awkward phrasing. Minor grammar and punctuation corrections are needed throughout. Improving clarity in the description of the fuzzy logic system and simulation steps would benefit the reader. Maybe you could seek some proof-reading?

Author Response

Comments and Suggestions for Authors

The manuscript introduces a relevant and technically solid fuzzy-PID control strategy for photovoltaic (PV)-based EV charging systems.

The simulation approach is thorough and clearly presented, with well-defined metrics and consistent results. The adaptive switching frequency control is a valuable contribution that enhances system efficiency and reliability. However, the study overstates its readiness for real-world deployment, as no hardware validation or prototype testing is presented.

Please consider tempering such claims in the abstract and conclusion. Additionally, the manuscript includes a relatively high number of self-citations, which should in my opinion be reduced or more clearly justified.

Finally, some figures would also benefit from improved label clarity. Minor inconsistencies in mathematical notation (e.g., subscripts and variable names) should be addressed for readability.

Overall it's a good work, I think a minor revision would do the trick to get this manuscript to a publishable state.

 

Author Responses:

We sincerely thank the reviewer for the constructive and positive evaluation of our manuscript. We appreciate the acknowledgment of the technical relevance, clarity of the simulation approach, and contribution of the adaptive switching-frequency control strategy.

Comments on the Quality of English Language

The English is generally clear but can be improved for precision and fluency. Some sentences are overly long or contain awkward phrasing. Minor grammar and punctuation corrections are needed throughout. Improving clarity in the description of the fuzzy logic system and simulation steps would benefit the reader. Maybe you could seek some proof-reading?

Author Responses:

We thank the reviewer for the helpful observation regarding the clarity and fluency of the English language. In response, we have thoroughly revised the manuscript to improve readability, precision, and grammar. Long or complex sentences have been simplified, awkward phrasing has been corrected, and punctuation has been standardized throughout.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters
In this paper, the authors propose a fuzzy logic based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. The presentation of the paper should be improved. I have listed several comments as follows:
1-    In the literature, several researchers proposed a hybrid fuzzy logic PID controllers. For this reason, the authors need to highlight their major contribution clearly. 
2-    In Fig. 1, the power circuit of the studied system should be presented.
3-    Figure 2 do not present the integration of Fuzzy logic with PID clearly. 
4-    Membership functions of the inputs and output are missing. 
5-    Page 8, line 330, the authors test the studied system under critical conditions (sudden change in the solar irradiation and so on) but there are no results presented in the paper.
6-    The authors do not include any comparative study with other existing methods. 
7-    A table that includes all system parameters should be presented. 
8-    The simulation results should be validated by using an HIL or experimental setup. 

Author Response

• In the literature, several researchers proposed a hybrid fuzzy logic PID controllers. For this reason, the authors need to highlight their major contribution clearly.

Author Responses:

We agree with the reviewer. The Introduction and Novelty sections have been revised to explicitly highlight the unique contributions of this work.

• In Fig. 1, the power circuit of the studied system should be presented.

Author Responses:

We thank the reviewer for this suggestion. Figure 1 has been updated to include a complete power-stage diagram.

• Figure 2 do not present the integration of Fuzzy logic with PID clearly.

Author Responses:

Figure 2 has been redrawn to clearly present the integration of the fuzzy logic module with the PID regulator.

• Membership functions of the inputs and output are missing.

Author Responses:

Membership functions are shown in Table-1

• Page 8, line 330, the authors test the studied system under critical conditions (sudden change in the solar irradiation and so on) but there are no results presented in the paper.

Author Responses:

These results are already presented in the manuscript. The system response under sudden changes in solar irradiance and dynamic load conditions is included in Section 3.1 System Behavior under Variable Irradiance

• The authors do not include any comparative study with other existing methods.

Author Responses:

Comparison is provided in Section 3.4 and Table 5.

• A table that includes all system parameters should be presented.

Author Responses:

A system-parameter table is included in the manuscript in Table 2

8-    The simulation results should be validated by using an HIL or experimental setup

Author Responses:

HIL validation is not included; however, the manuscript already clarifies that HIL testing is part of future work.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript proposes a fuzzy logic–based PID controller (fuzzy-PID) for regulating EV battery charging powered by a photovoltaic (PV) system. The controller adaptively tunes PID gains based on error and error-rate, and additionally modulates switching frequency to improve efficiency. 

The approach is promising, but several points require clarification or additional evidence. I have several questions and concerns that the authors should address:

1. How does your fuzzy-PID controller differ fundamentally from existing fuzzy-PID approaches in the PV or EV charging literature? The novelty statement remains broad; please provide a clearer explanation of the unique technical contributions.
2. Why was a five-rule fuzzy system chosen? What optimization criteria or systematic tuning process determined this rule-base structure?
3. The paper currently presents only qualitative improvements. Could you provide quantitative results, such as actual efficiency values,  before and after switching-frequency modulation?
4. How feasible is the proposed controller for real-time implementation?

 

Author Response

1. How does your fuzzy-PID controller differ fundamentally from existing fuzzy-PID approaches in the PV or EV charging literature? The novelty statement remains broad; please provide a clearer explanation of the unique technical contributions.

Author Responses:

Thank you for the constructive comment. The manuscript has now clarified the fundamental differences between our fuzzy-PID controller and previously reported approaches.

In the revised manuscript, we emphasize that the novelty of our method lies in three technical contributions:

• Adaptive switching-frequency modulation, integrated directly into the fuzzy-PID loop, which dynamically lowers switching losses and device stress. Existing fuzzy-PID works in PV/EV literature usually tune only the gains, not the switching frequency.
• A complete resonant converter model, including LC resonant tank, high-frequency transformer, and full-wave rectifier, rather than simplified DC–DC models used in prior work.
• A lightweight five-rule fuzzy-PID design optimized for real-time use, demonstrating robust performance under highly variable irradiance and load conditions.

These points are explicitly added to strengthen the novelty statement.

1. Why was a five-rule fuzzy system chosen? What optimization criteria or systematic tuning process determined this rule-base structure?

Author Responses:

The reason for selecting a five-rule fuzzy system is now clearly stated in the manuscript. The reduced rule base was deliberately chosen to minimize computational complexity, enabling real-time implementation on low-cost DSP/ARM hardware. Although initial simulations included larger rule sets, we found that expanding the rule base did not yield significant performance improvement. Therefore, the five carefully selected rules adequately capture the dominant control dynamics while maintaining low processing burden. This selection was verified through iterative simulation-based tuning, ensuring optimal performance with minimal rule redundancy.

1. The paper currently presents only qualitative improvements. Could you provide quantitative results, such as actual efficiency values, before and after switching-frequency modulation?

Author Responses:

We appreciate this important observation. The current manuscript provides comparative performance trends (settling time, overshoot, ripple, disturbance response), but it does not yet include explicit numerical efficiency values before and after switching-frequency modulation. At this stage, the switching-loss and efficiency analysis is qualitative because the study is limited to a simulation environment. Numerical converter efficiencies require detailed switching-loss modeling or hardware-in-the-loop (HIL) measurement, which is planned for the next phase of the research.

Accordingly, we have added a statement clarifying that:

“Quantitative efficiency analysis including measured conversion efficiency improvement resulting from adaptive switching-frequency modulation will be included in our future HIL based validation work.”

1. How feasible is the proposed controller for real-time implementation?

Author Responses:

The manuscript now highlights that the proposed controller is designed to be feasible for real-time implementation. Several elements support this claim:

The fuzzy-PID uses only two inputs and five rules, significantly reducing computation time. The structure is lightweight and intentionally optimized for embedded microcontrollers. The paper states that the controller is suitable for DSP/ARM platforms, with real-time implementation planned as future work. The simulations already use discretized time steps (), consistent with real switching hardware. Therefore, real-time feasibility is confirmed in principle, and full hardware verification is part of the future HIL and prototype stage.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

1) Where, P_load is the power supplied..., 'Where should be lower case, without coma', such fortmat in the paper should be corrected. 

2) The novelty of the paper is not convincing, Line 98-116, 'The novelty of this study lies in the development and simulation of a real-time fuzzy  logic based PID control strategy specifically designed for photovoltaic (PV)-powered electric vehicle (EV) charging systems operating under uncertain and rapidly changing environmental conditions.  This is not new and it is the common sense to build the simulation model for the verification. Therefore, the key contribution of the paper is lack of techical innovation. Should the authors to justify the new idea in the paper, otherwise, the paper should not be accepted.  

3) Fuzzy-PID is not new, the authors should point out the advantages and difference compared to other current methods.  

Comments on the Quality of English Language

Can be imprvoed, especailly the format. 

 

Author Response

Comments and Suggestions for Authors

(1) Where, P_load is the power supplied..., 'Where should be lower case, without coma', such fortmat in the paper should be corrected. 

Authors’ response

We agree with the reviewer. The formatting has been corrected throughout the manuscript. Specifically:

• “Where,” has been revised to “where” (lowercase, without a comma).
• Similar formatting issues in equations and explanatory text have been systematically corrected for consistency and readability.

2) The novelty of the paper is not convincing, Line 98-116, 'The novelty of this study lies in the development and simulation of a real-time fuzzy  logic based PID control strategy specifically designed for photovoltaic (PV)-powered electric vehicle (EV) charging systems operating under uncertain and rapidly changing environmental conditions.  This is not new and it is the common sense to build the simulation model for the verification. Therefore, the key contribution of the paper is lack of techical innovation. Should the authors to justify the new idea in the paper, otherwise, the paper should not be accepted.  

Authors’ response

We appreciate the reviewer’s concern and agree that the original novelty statement required clearer technical justification. Accordingly, the novelty description in Lines 98-116 has been substantially revised to explicitly highlight the technical contributions beyond common fuzzy-PID simulations.

The revised manuscript clarifies that the contribution just does not lie in the use of fuzzy-PID itself, but in its adaptive switching-frequency modulation, which is rarely considered in existing EV charging studies. The controller is implemented on a detailed resonant LC converter with transformer isolation, rather than simplified models, and employs a lightweight fuzzy structure suitable for real-time DSP/ARM implementation. Robustness is evaluated under highly variable irradiance and dynamic load conditions, including multi-EV scenarios. These revisions have been incorporated into Lines 98-116 and clearly justify the technical contribution beyond conventional fuzzy-PID simulations. In this revised manuscript, our changes are highlighted as follows:

(Line 98 to line 114)

“The contribution of this study does not lie in the standalone use of fuzzy-PID control, which has been widely reported in the literature, but in its application to adaptive switching-frequency regulation in a photovoltaic (PV) powered electric vehicle (EV) charging system with a resonant power conversion topology. The proposed control strategy integrates fuzzy logic based real time tuning of PID gains with dynamic switching-frequency modulation to reduce switching losses, electromagnetic interference, and power-device stress while maintaining stable output voltage regulation.

Unlike many existing studies that employ simplified or averaged converter models, this work implements the controller on a detailed resonant LC converter including a high-frequency transformer and full wave rectifier, enabling realistic evaluation of soft switching behavior and converter level dynamics. Furthermore, the fuzzy inference system is designed with a reduced rule base and simple membership functions, ensuring low computational complexity and suitability for real time implementation on DSP or ARM based platforms. The robustness of the proposed approach is assessed under highly variable solar irradiance and dynamic load conditions, including multi-EV charging scenarios. These features collectively distinguish the proposed method from conventional fuzzy-PID-based EV charging control strategies reported in the literature.”

3) Fuzzy-PID is not new, the authors should point out the advantages and difference compared to other current methods.  

Authors’ response

We agree with the reviewer that fuzzy-PID control itself is not a new concept. However, as summarized in Table 1, existing fuzzy-based EV charging and energy management studies primarily focus on charging coordination, power allocation, routing, or SOC management, and they do not consider adaptive switching-frequency control or detailed resonant converter modeling. In this revised manuscript, our changes are highlighted as follows:

(Line 125~131 _ Table 1.)

Table 1. Comparison of Related Fuzzy-Based EV Charging and Energy Management Studies

Ref.	Year	Description (Focus)	Applied Method	Switching Frequency Adaptation	Resonant Converter Modeling	Real-Time Feasibility	Robustness under Uncertainty	Experimental / HIL
[27]	2016	Optimized fuzzy V2G control for grid frequency regulation	Optimized Fuzzy Controller	×	×	✓	✓	×
[3]	2019	Fuzzy inference–based power allocation for EV parking lots under grid constraints	Fuzzy Logic Inference Algorithm	×	×	✓	✓	×
[14]	2020	Fuzzy energy management for coordinated EV charging in LV distribution networks	Fuzzy Energy Management	×	×	✓	✓	×
[29]	2020	Fuzzy coordination of EV charging considering distance from substation	Fuzzy Logic Management	×	×	✓	✓	×
[16]	2021	Fuzzy controllers for EV charging/discharging and V2G operation in smart grids	Fuzzy Logic Controllers	×	×	✓	✓	×
[1]	2022	Fuzzy logic–based smart charging decision support for EV users in smart grids	Fuzzy Logic Controller	×	×	✓	✓	×
[28]	2022	Fuzzy SOC-based energy management for hybrid EV power systems	Fuzzy Energy Management + PF	×	×	✓	✓	×
[12]	2024	Neuro-fuzzy and ML-based EV routing and charging station selection	Neuro-Fuzzy + ML	×	×	×	✓	×
This study	2025	Adaptive fuzzy-PID control with switching-frequency modulation for PV-powered EV charging using a detailed resonant converter	Fuzzy-PID with adaptive switching frequency	✓	✓	✓	✓	×

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper is accepted for publication in current form.

Author Response

Comments and Suggestions for Authors

The paper is accepted for publication in current form.

Authors’ response

We are grateful for the final acceptance of our paper. We thank the Editor for the positive feedback and the recommendation for publication.

Reviewer 4 Report

Comments and Suggestions for Authors

The authors have adequately addressed the previous comments, and I recommend publication

Author Response

Comments and Suggestions for Authors

The authors have adequately addressed the previous comments, and I recommend publication

Authors’ response

We are grateful for the positive assessment and the recommendation to publish our manuscript.