Performance Evaluation and Accuracy Analysis of a Chassis Dynamometer for Light Electric Vehicles

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript investigates the design, development, and performance evaluation of a chassis dynamometer for light electric vehicles (LEVs) using the Prony brake method for torque measurement. The study provides detailed experimental results on the accuracy and consistency of measurements for parameters such as RPM, torque, and brake horsepower (BHP). While the manuscript addresses an important topic in the field of sustainable urban transportation, several areas require significant improvements to ensure the study meets the standards for scientific publication.

• While the manuscript presents a technical evaluation of a chassis dynamometer, the novelty of the study is not clearly articulated. The authors should clarify how their approach advances the field beyond existing chassis dynamometer designs and methodologies.
• The manuscript focuses on using the Prony brake and magnetic proximity sensors, but a deeper discussion comparing this setup to alternative techniques (e.g., rotary encoders or strain gauge-based systems) is missing.

• Experimental Design: The experimental setup lacks sufficient detail regarding the calibration process for sensors and the environmental conditions during testing (e.g., temperature or humidity). These factors could influence the accuracy of RPM and torque measurements.
• Error Analysis: The analysis of standard deviation and error is provided but does not include confidence intervals or statistical significance tests. Incorporating these would enhance the robustness of the findings.
• Validation: The study mentions that RPM and torque measurements were validated using tachometers and torque gauges, but the methodology for this validation (e.g., number of trials, calibration of reference instruments) needs further elaboration.

• The manuscript lacks discussion on the practical scalability of the proposed dynamometer design. For instance, how feasible is it to adapt this setup for higher-power electric vehicles or different vehicle categories (e.g., heavy-duty EVs)?
• The absence of a sensitivity analysis for key parameters, such as sensor alignment or external disturbances (e.g., vibrations), limits the practical applicability of the results.

• Figures and tables are informative but could be improved with better annotations. For example:
• In Figure 8, labeling the RPM ranges and highlighting trends in standard deviation would help readers interpret the results more effectively.
• The axes in Figures 10 and 12 should include units and scaling for clarity.
• Tables summarizing the accuracy and precision of sensors (e.g., standard deviation, mean error) should include error margins or additional details on variability.

• The manuscript contains grammatical issues and awkward phrasing. For instance:
• Original: "The torque measurement test was conducted by comparing the torque readings from the torque meter with those from the torque sensor using the Prony brake system." Suggested: "Torque measurement tests were conducted by comparing the Prony brake system's torque sensor readings with those obtained from a reference torque meter."
• Original: "Variations in test results can be attributed to differences in operation by various operators, which may affect the consistency and accuracy of the measurements." Suggested: "Operator variability may have contributed to inconsistencies in the test results, affecting measurement accuracy."
• A thorough proofreading and language review is necessary to improve readability and professionalism.

·      The abstract provides a good overview of the study but could include specific quantitative results, such as average errors or improvements in measurement accuracy.

·      While the introduction outlines the importance of LEVs and dynamometer testing, it lacks a focused statement on the specific research gap addressed by the study.

·      The references include several relevant studies, but there is a lack of citations from recent years. Including more up-to-date references, especially from 2021 onward, would strengthen the literature review.

·      Some diagrams (e.g., Figure 6, the flowchart of the electrical components) are difficult to interpret due to insufficient labeling. Adding detailed legends or explanations in the captions would improve clarity.

·      The discussion primarily summarizes results without adequately interpreting their broader significance. For example:

o   What implications do the findings have for the design of future LEVs?

o   How can the observed error trends be minimized in practical applications?

 

The manuscript presents valuable insights into the development of chassis dynamometers for LEVs but requires significant revisions in the following areas:

1. Expanding the methodological detail to improve reproducibility and rigor.
2. Enhancing the discussion of novelty, scalability, and practical implications.
3. Improving the clarity and presentation of figures, tables, and overall writing.

With these revisions, the study has the potential to make a meaningful contribution to the field of EV testing and performance evaluation.

I hope these comments will be helpful to the authors.

Author Response

Dear Sir, 

Thank you for your review and suggestion

• (Comment) While the manuscript presents a technical evaluation of a chassis dynamometer, the novelty of the study is not clearly articulated. The authors should clarify how their approach advances the field beyond existing chassis dynamometer designs and methodologies.

(Respond - added to script) In this study, we developed a chassis dynamometer specifically designed for Light Electric Vehicles (LEV) that not only measures vehicle performance but also enhances precision in torque and RPM measurements through the integration of the Prony brake method with magnetic proximity sensors. This approach introduces several novel advancements over existing dynamometer designs:

1. Improved Measurement Accuracy: Our system utilizes magnetic proximity sensors that offer high accuracy in RPM measurements, particularly at low speeds, addressing the inaccuracies commonly encountered in existing dynamometers at higher speeds. Additionally, the integration of the Prony brake mechanism with a load sensor improves the precision of torque measurements, a method not widely implemented in LEV testing.
2. Adaptability to Real-World Conditions: The twin roller dynamometer we employ enables the simulation of real-world driving conditions, including various road inclinations and surface textures. This expands the application of the dynamometer to test LEVs under a wide range of urban environmental conditions, which is critical for optimizing electric vehicle performance.
3. Compact and Space-Efficient Design: Compared to larger dynamometer systems, our design is more compact, allowing it to be used in research and development facilities with limited space. This makes the dynamometer more effective for testing LEVs, which are often developed in urban settings where space constraints are common.

Thus, this study contributes novel advancements in the design of a chassis dynamometer for LEVs, offering more precise measurements, adaptability to diverse road conditions, and a space-efficient solution, which represents a significant improvement over previous design

• (Comment) The manuscript focuses on using the Prony brake and magnetic proximity sensors, but a deeper discussion comparing this setup to alternative techniques (e.g., rotary encoders or strain gauge-based systems) is missing.

(Respond - added to script) to compare our approach with alternative techniques commonly used in chassis dynamometers, such as rotary encoders for RPM measurement and strain gauge-based systems for torque measurement, to provide a deeper understanding of the advantages and trade-offs.

1. RPM Measurement: Magnetic Proximity Sensors vs. Rotary Encoders Magnetic proximity sensors, as utilized in our setup, are highly effective in environments where non-contact measurements are required, offering durability and minimal wear over time. This is particularly advantageous in harsh testing conditions, as proximity sensors are less prone to mechanical failure. However, rotary encoders are often considered superior in terms of absolute accuracy, particularly at higher rotational speeds. Encoders measure angular displacement directly, often with higher resolution than proximity sensors, which rely on pulse counting. Although rotary encoders may provide more detailed measurements, their mechanical complexity and potential for wear over time can limit their application in environments requiring low maintenance, where our magnetic sensor setup proves more robust.
2. Torque Measurement: Prony Brake vs. Strain Gauge-Based Systems The Prony brake mechanism used in our dynamometer setup offers a simplified and cost-effective means of measuring torque, which is well-suited to the low to medium torque ranges typical of Light Electric Vehicles (LEVs). By applying a controlled resistance and measuring the resultant force, this method provides a reliable estimate of torque without requiring complex electronic systems. On the other hand, strain gauge-based systems offer a higher level of sensitivity and precision, particularly for dynamic measurements. Strain gauges can detect minute deformations in a material and provide real-time feedback on torque fluctuations. However, they often require more sophisticated calibration and are more susceptible to environmental factors such as temperature changes, which can introduce errors. In comparison, the Prony brake system, while not as precise at detecting small variations, offers stable and repeatable results under controlled testing conditions, making it ideal for the steady-state performance evaluations conducted in this study

• (Comment) Experimental Design: The experimental setup lacks sufficient detail regarding the calibration process for sensors and the environmental conditions during testing (e.g., temperature or humidity). These factors could influence the accuracy of RPM and torque measurements

(Respond - Added to script) The calibration process used for both the magnetic proximity sensors and the Prony brake mechanism, as well as the steps taken to account for environmental conditions during testing.

1. Calibration of RPM Sensors

The magnetic proximity sensors used to measure RPM were calibrated using a high-precision tachometer as a reference. This tachometer was mounted on the rotating shaft of the test vehicle, and multiple trials were conducted across a range of speeds (from 100 RPM to 800 RPM) to ensure the sensor readings were consistent with the tachometer's output. The calibration process involved:

1. Recording RPM values at incremental speed levels and comparing the proximity sensor’s output to the tachometer’s readings.
2. Calculating the percentage error at each speed increment and adjusting the sensor’s output via software calibration to align with the reference values.
3. This process was repeated across five trials to ensure repeatability and accuracy in the measurements.

1. Calibration of Torque Measurement (Prony Brake System)

The Prony brake system was calibrated using a torque gauge with known accuracy as the reference instrument. The torque gauge was attached to the same shaft as the Prony brake and readings were compared under controlled load conditions. The calibration steps included:

1. Applying known loads to the Prony brake and measuring the resultant torque using the gauge.
2. Adjusting the sensor readings based on the deviation observed between the Prony brake output and the torque gauge reference values.
3. Multiple tests were performed across different torque levels to ensure accuracy, particularly in the low-torque range where measurement precision is more challenging.

1. Environmental Conditions

During the testing process, temperature and humidity were monitored and controlled to minimize their potential influence on sensor accuracy. The following measures were implemented:

1. The tests were conducted in a temperature-controlled environment with an average temperature of 25°C to ensure that thermal expansion or contraction did not affect the sensor performance or introduce noise in the measurements. This was particularly important for the strain gauges in the load sensors, which are sensitive to temperature variations.
2. Humidity levels were kept within a range of 40-50%, as higher humidity can affect electrical resistance in sensors and potentially degrade the accuracy of electronic components, including the proximity sensors and the Prony brake load sensor.
3. Any temperature fluctuations during the testing were recorded, and the results were analyzed to determine if environmental changes had any significant effect on the measurement accuracy.

By ensuring proper calibration of the sensors and controlling environmental factors, we minimized potential sources of error in both RPM and torque measurements, providing more reliable and repeatable data for the performance evaluation of light electric vehicles.

• (Comment) Error Analysis: The analysis of standard deviation and error is provided but does not include confidence intervals or statistical significance tests. Incorporating these would enhance the robustness of the findings. 

confidence intervals, and statistical significance tests:

1. Confidence Interval

The confidence interval is used to provide a range of values that are likely to contain the population parameter, based on the sample data. In your case, it can be applied to the RPM or torque measurements to determine how confident we are about the average values.

Formula for Confidence Interval (CI):

The CI provides a range within which the true population mean is expected to fall, with a certain confidence level (e.g., 95%).

2. Standard Error of the Mean (SEM)

The standard error indicates how far the sample mean is likely to be from the true population mean. The smaller the standard error, the more accurate the estimate of the mean.

Formula for Standard Error:

3. Statistical Significance Testing

To verify whether the differences observed between the instrument measurements (e.g., torque sensor and tachometer) and the reference instrument are statistically significant, a t-test can be used. The t-test evaluates whether the means of two datasets differ significantly

Formula for t-test (for two independent samples):

If the t-value is greater than the critical value at a given significance level (e.g., 0.05), we can conclude that the difference is statistically significant

4. Relative Error

To assess how much the measurement deviates from the true value, relative error can be used. This helps determine how large the error is compared to the actual value being measured.

Formula for Relative Error:

• (Comment) Validation: The study mentions that RPM and torque measurements were validated using tachometers and torque gauges, but the methodology for this validation (e.g., number of trials, calibration of reference instruments) needs further elaboration. 

(Respond - kept) Here are methods and theories commonly used for validation in scientific research, especially in the context of validating measurement instruments like the chassis dynamometer in your study:

1. Comparison with a Reference Instrument

One of the most fundamental methods of validation is comparing the measurements from the experimental device (e.g., the chassis dynamometer) to a known, calibrated reference instrument. In your case, this could involve comparing RPM readings with a tachometer or torque values with a high-precision torque gauge.

Steps:

Select a Reference Standard: Choose an instrument that is well-calibrated and widely recognized for accuracy (e.g., a certified tachometer or torque gauge).

Run Comparative Tests: Conduct tests under the same conditions using both the chassis dynamometer and the reference instrument.

Analyze Differences: Calculate the differences (errors) between the dynamometer readings and the reference values, and evaluate the consistency and magnitude of these differences.

2. Repeatability Testing

Repeatability refers to the ability of an instrument to produce consistent results when the same measurements are repeated under identical conditions. This is critical for ensuring that the dynamometer produces reliable data.

Steps:

1. Conduct Multiple Trials: Perform the same measurement multiple times, under the same conditions, using the same setup.
2. Calculate Standard Deviation: Use the standard deviation of the repeated measurements to quantify the variability.
3. Assess Repeatability: Lower standard deviation indicates better repeatability.

 

3. Reproducibility Testing

Reproducibility refers to the ability of the instrument to produce consistent results when measurements are taken under different conditions, such as by different operators or in different environments. This is important for generalizing the performance of the instrument beyond the original test setting.

Steps:

Vary Conditions: Have different operators run the test, or run the tests in different settings (e.g., different times of day, or slight variations in setup).

Compare Results: Analyze the consistency of the results across different conditions by calculating the variance or standard deviation.

The manuscript lacks discussion on the practical scalability of the proposed dynamometer design. For instance, how feasible is it to adapt this setup for higher-power electric vehicles or different vehicle categories (e.g., heavy-duty EVs)? à instrument no problem, heavy sedikit modif di mechanical

• The absence of a sensitivity analysis for key parameters, such as sensor alignment or external disturbances (e.g., vibrations), limits the practical applicability of the results. àaccording to device/sensor
• Figures and tables are informative but could be improved with better annotations. For example:
• • Figure 8 - Done
  • The axes in Figures 10 and 12 should include units and scaling for clarity - Done
• Tables summarizing the accuracy and precision of sensors (e.g., standard deviation, mean error) should include error margins or additional details on variability.
• The manuscript contains grammatical issues and awkward phrasing. For instance:
  • Original:"The torque measurement test was conducted by comparing the torque readings from the torque meter with those from the torque sensor using the Prony brake system." Suggested: "Torque measurement tests were conducted by comparing the Prony brake system's torque sensor readings with those obtained from a reference torque meter." (Done)
  • Original:"Variations in test results can be attributed to differences in operation by various operators, which may affect the consistency and accuracy of the measurements." Suggested: "Operator variability may have contributed to inconsistencies in the test results, affecting measurement accuracy." (Done)
• A thorough proofreading and language review is necessary to improve readability and professionalism.
• The abstract provides a good overview of the study but could include specific quantitative results, such as average errors or improvements in measurement accuracy.
• While the introduction outlines the importance of LEVs and dynamometer testing, it lacks a focused statement on the specific research gap addressed by the study.
• The references include several relevant studies, but there is a lack of citations from recent years. Including more up-to-date references, especially from 2021 onward, would strengthen the literature review.
• Some diagrams (e.g., Figure 6, the flowchart of the electrical components) are difficult to interpret due to insufficient labeling. Adding detailed legends or explanations in the captions would improve clarity. (Done)
• The discussion primarily summarizes results without adequately interpreting their broader significance. For example:

o   What implications do the findings have for the design of future LEVs? 

o   How can the observed error trends be minimized in practical applications?

Additional - explained in the script

 

The manuscript presents valuable insights into the development of chassis dynamometers for LEVs but requires significant revisions in the following areas:

1. Expanding the methodological detail to improve reproducibility and rigor.
2. Enhancing the discussion of novelty, scalability, and practical implications.
3. Improving the clarity and presentation of figures, tables, and overall writing.

Reviewer 2 Report

Comments and Suggestions for Authors

The study addresses an important topic in evaluating light electric vehicles (LEVs), which aligns with the global push for sustainable mobility solutions. The use of a chassis dynamometer with the Prony brake method is relevant for precise torque and power measurement, contributing to EV testing methodologies.

The study provides a structured experimental setup with clear measurement techniques. Use of multiple validation methods (magnetic proximity sensor, tachometer, Prony brake) ensures reliability. The comparison of measurement errors for RPM and torque provides insight into the accuracy of the system.

The statistical analysis (error, standard deviation) adds depth to the evaluation.

Identifies an increasing standard deviation in RPM measurements at higher speeds, highlighting sensor limitations.

The paper follows a clear structure (Abstract, Introduction, Methodology, Results, Conclusion).

 

But :

 

There is a Lack of a clear research gap, The introduction explains the benefits of EVs and the importance of LEV testing, but it does not clearly state what gaps in the current knowledge this study fills, You can add a short paragraph summarizing existing dynamometer technologies and their limitations.

For literature review part, While some references are included, additional citations discussing accuracy limitations in current dynamometers would strengthen the introduction.

The study presents the average error and standard deviation, but it does not clearly specify, first how measurement errors were calculated, and also the uncertainty range for each sensor type.

There is also an Experimental Conditions Missing, because there is no discussion on ambient conditions (temperature, humidity, vibrations) that could affect the sensor readings.

 

Come to the results part

The standard deviation increases at higher RPMs, but why? , You need to add a discussion on potential reasons for this trend (sensor lag, electromagnetic interference, or mechanical vibrations) would add clarity.

There is no reference to existing industry standards for chassis dynamometer accuracy.

The paper does not mention any potential improvements or future research directions. I Suggest to add a discussion on possible next steps, such as integration with real-time AI-driven calibration, and Testing under real-world road conditions.

 1.       How can your study be applied in the development of future light electric vehicles?

2.       What improvements would you recommend to enhance the accuracy of your test bench?

3.       Have you considered integrating an AI-based system to correct errors in real-time?

4.       What are the next steps in your research? Do you plan to conduct tests under real-world conditions?

The study demonstrates several strengths, including a well-structured methodology, a relevant topic with practical applications, and a clear presentation of results supported by thorough error analysis. However, there are areas that require improvement. The research gap needs to be more explicitly defined to highlight the study's contribution. Additionally, the error calculation methods should be clarified to ensure transparency and reproducibility. A deeper analysis of performance trends would enhance the discussion, particularly regarding variations in measurement accuracy. Finally, comparing the results with industry standards would strengthen the study’s validity and contextual relevance.

Author Response

Dear Sir,

Thank you for your review and suggestion. The responds have already added to the script.

 

The study addresses an important topic in evaluating light electric vehicles (LEVs), which aligns with the global push for sustainable mobility solutions. The use of a chassis dynamometer with the Prony brake method is relevant for precise torque and power measurement, contributing to EV testing methodologies.

The study provides a structured experimental setup with clear measurement techniques. Use of multiple validation methods (magnetic proximity sensor, tachometer, Prony brake) ensures reliability. The comparison of measurement errors for RPM and torque provides insight into the accuracy of the system.

The statistical analysis (error, standard deviation) adds depth to the evaluation.

Identifies an increasing standard deviation in RPM measurements at higher speeds, highlighting sensor limitations.

The paper follows a clear structure (Abstract, Introduction, Methodology, Results, Conclusion).

 

But :

 

There is a Lack of a clear research gap, The introduction explains the benefits of EVs and the importance of LEV testing, but it does not clearly state what gaps in the current knowledge this study fills, You can add a short paragraph summarizing existing dynamometer technologies and their limitations.
Summary of Existing Dynamometer Technologies and Their Limitations

Dynamometers are essential tools for evaluating vehicle performance, with several existing technologies available, each with its own strengths and limitations. Commonly used dynamometers include the eddy current, hydraulic, and Prony brake dynamometers. Eddy current dynamometers utilize electromagnetic induction to create resistance, offering precise control and rapid response times; however, they require complex cooling systems and are costly. Hydraulic dynamometers generate resistance through fluid dynamics, making them robust for high-power applications but less precise in low-speed measurements. Prony brake dynamometers, which operate by applying friction to measure torque, are simple and cost-effective but prone to heat buildup and wear, affecting long-term accuracy. While these technologies have been widely used in internal combustion engine testing, their applicability to electric vehicles (EVs) is limited due to differences in torque characteristics and regenerative braking, necessitating further advancements in dynamometer design for accurate EV performance assessments.

For literature review part, While some references are included, additional citations discussing accuracy limitations in current dynamometers would strengthen the introduction.

The study presents the average error and standard deviation, but it does not clearly specify, first how measurement errors were calculated, and also the uncertainty range for each sensor type.
“Measurement errors in dynamometer testing are primarily assessed by comparing sensor outputs against known reference values. For each type of sensor used in the system, errors were calculated using the absolute deviation method, where the recorded values were subtracted from the reference values and then averaged over multiple trials. Additionally, percentage error calculations were conducted to express deviations in relative terms.

 

The uncertainty range for each sensor type was determined using statistical analysis, incorporating both systematic and random errors. For the RPM sensor, uncertainty was assessed based on the standard deviation of repeated measurements, with an observed variation of ±0.55 RPM. The torque sensor uncertainty was analyzed using calibration procedures against a known torque standard, yielding an uncertainty range of ±0.03 Nm. These values provide insight into the reliability of the measurements and highlight areas where sensor accuracy can be improved through calibration and enhanced signal processing techniques

There is also an Experimental Conditions Missing, because there is no discussion on ambient conditions (temperature, humidity, vibrations) that could affect the sensor readings.

 

Come to the results part

The standard deviation increases at higher RPMs, but why? , You need to add a discussion on potential reasons for this trend (sensor lag, electromagnetic interference, or mechanical vibrations) would add clarity.

There is no reference to existing industry standards for chassis dynamometer accuracy.

The paper does not mention any potential improvements or future research directions. I Suggest to add a discussion on possible next steps, such as integration with real-time AI-driven calibration, and Testing under real-world road conditions.

To enhance the accuracy and applicability of dynamometer testing, several advancements can be explored. One promising direction is the integration of real-time AI-driven calibration systems. By utilizing machine learning algorithms, the dynamometer can continuously adjust and optimize sensor readings based on historical data and real-time feedback, significantly improving measurement precision and reducing errors caused by environmental factors.

Additionally, testing under real-world road conditions should be conducted to validate dynamometer performance beyond controlled laboratory environments. This would involve deploying instrumented test vehicles on various terrains and driving conditions, allowing for direct comparison of dynamometer-based data with actual road performance. Such real-world validation can provide valuable insights into the practical limitations of the dynamometer and help refine testing methodologies to better simulate real-world usage scenarios.

Further research can also focus on enhancing sensor fusion techniques, where multiple sensor inputs are combined to provide more reliable and accurate measurements. By integrating high-precision optical and inertial sensors, dynamometer systems can achieve higher accuracy levels, making them more suitable for advanced EV testing and performance optimization

1. How can your study be applied in the development of future light electric vehicles?

The findings from this study can significantly contribute to the future development of LEVs by providing a robust platform for performance evaluation. The chassis dynamometer developed here allows for detailed assessments of electric motor performance, battery efficiency, and energy consumption under simulated urban driving conditions. These insights can be applied to optimize vehicle designs, particularly in terms of motor efficiency, torque management, and energy recovery systems. As electric vehicle technology evolves, integrating more advanced testing methods, such as regenerative braking simulations, could further aid in the refinement of LEV designs, making them more efficient and better suited for city environments.

2. What improvements would you recommend to enhance the accuracy of your test bench?

Although the Prony brake and magnetic proximity sensor systems used in this study have demonstrated high accuracy, there are several enhancements that could improve the precision of the test bench:

1. Integration of Rotary Encoders: For RPM measurements, incorporating rotary encoders with higher resolution could provide even more precise rotational speed data, particularly at higher RPMs, where proximity sensors may show increased variability.
2. Strain Gauge-Based Torque Measurement: To enhance the precision of torque measurements, especially in dynamic conditions, adding a strain gauge-based system could offer real-time, high-sensitivity torque readings. This would complement the Prony brake system, allowing for more dynamic load testing and improving the test bench’s applicability for a wider range of LEV performance evaluations.
3. Have you considered integrating an AI-based system to correct errors in real-time?

AI-based systems could also be used to optimize testing conditions by adjusting environmental parameters (e.g., temperature, load conditions) automatically based on the real-time performance of the vehicle being tested.

One of the promising future directions for improving the accuracy and responsiveness of the dynamometer setup is the integration of AI-driven calibration systems. By utilizing machine learning algorithms, it would be possible to:

a. Correct sensor inaccuracies in real-time by continuously comparing sensor data with reference models and making dynamic adjustments during testing.

b. Implement predictive analytics to anticipate sensor drift or mechanical wear, ensuring that the testing system remains calibrated without the need for frequent manual adjustments.

4. What are the next steps in your research? Do you plan to conduct tests under real-world conditions?

future research should focus on validating the dynamometer’s performance under real-world road conditions. This could include:

1. Conducting tests on actual road surfaces, where variables such as road friction, slope, and weather conditions (e.g., rain, heat) could impact vehicle performance in ways that may not be fully captured in a laboratory environment.
2. Field testing using portable versions of the chassis dynamometer, which would allow for on-site evaluation of LEVs directly in urban settings. This would provide more realistic insights into vehicle performance, particularly in terms of energy efficiency, handling, and safety under various real-world scenarios.
3. Further research into regenerative braking and energy recovery systems during these real-world tests would provide critical data to improve the energy efficiency of future LEVs.

The study demonstrates several strengths, including a well-structured methodology, a relevant topic with practical applications, and a clear presentation of results supported by thorough error analysis. However, there are areas that require improvement. The research gap needs to be more explicitly defined to highlight the study's contribution. Additionally, the error calculation methods should be clarified to ensure transparency and reproducibility. A deeper analysis of performance trends would enhance the discussion, particularly regarding variations in measurement accuracy. Finally, comparing the results with industry standards would strengthen the study’s validity and contextual relevance. (Thank you)

Reviewer 3 Report

Comments and Suggestions for Authors

This paper focuses on the development of a chassis dynamometer for light electric vehicles (LEVs), utilizing the Prony brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of light electric vehicles under controlled conditions. The main shortcomings of the paper are shown as:

1. The actual road conditions of vehicles are complex and varied, such as changes in slope, differences in road surface roughness, straight lines and curves, etc. How did the author simulate these operating conditions?

2. The author needs to supplement the details of laboratory data processing in the paper.

3. The author needs to supplement photos of the experimental setup in the paper.

Author Response

Dear Sir,

Thank you for your review and suggestion. The revision has already added to the script.

This paper focuses on the development of a chassis dynamometer for light electric vehicles (LEVs), utilizing the Prony brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of light electric vehicles under controlled conditions. The main shortcomings of the paper are shown as:

1. The actual road conditions of vehicles are complex and varied, such as changes in slope, differences in road surface roughness, straight lines and curves, etc. How did the author simulate these operating conditions?

To replicate real-world vehicle performance, the authors simulated operating conditions using a controlled test environment on a chassis dynamometer. The test setup included a range of load conditions, varying RPM levels, and torque applications to assess the system's response under different driving scenarios. A 3 kW Brushless DC (BLDC) motor was used to simulate an electric vehicle drivetrain, with sensors placed to capture real-time data on speed, torque, and power output.

 

Furthermore, environmental variables such as resistance, temperature fluctuations, and energy regeneration were accounted for to ensure the accuracy of the simulation. The Prony brake method was implemented to provide controlled braking forces, mimicking real-world acceleration and deceleration patterns. To enhance reliability, multiple test cycles were conducted, and the collected data was analyzed using statistical techniques to determine performance consistency and sensor accuracy. These simulations provided essential insights into the dynamometer's ability to assess electric vehicle performance in a laboratory setting.

2. The author needs to supplement the details of laboratory data processing in the paper.
3. The author needs to supplement photos of the experimental setup in the paper.

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Editor

Concerns the MS entitled: "Performance Evaluation and Accuracy Analysis Of A Chassis Dynamometer For Light Electric Vehicles" will considered for publication in WEVJ.

The MS focuses on the development of a chassis dynamometer for light electric vehicles (LEV), utilizing the Prony brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of LEVs under controlled conditions. The dynamometer's performance evaluation revealed an average error of 0.55 for RPM readings, indicating a moderate level of variability in the sensor's accuracy. In contrast, the torque measurement yielded a significantly lower average error of 0.03, demonstrating high precision in capturing torque data. 

It is interested in the electric vehicles subject but needs some modifications before considering for publications as follows:

-Abstract is poor,it must put in more details to display the new results added and the comparison between the present investigation and the previous.

-Keywords only 3, it must at least 6 words.

-In introduction section, all cited refs must indicate to the authors as example: "fossil-fuel-powered 26 vehicles[1]." must modify to "fossil-fuel-powered 26 vehicles discussed by Alanazi [1]" and so on.

-All symbols used in Equations must ensure inserted as nomenclature.

-Figure 6. Electrical component flowchart needs more details.

-Figure 8 doesn't clear, it needs more resolution.

-It is too interested if authors make simulation between the results obtained and the previous obtained by others.

-Conclusion must put in more details.

-Refs. list needs more recently references in the MS field.

Comments on the Quality of English Language

English language needs revising by professional person in English.

Author Response

Dear Sir,

Thank you for your review and suggestion. The change has already added to the script.

 

It is interested in the electric vehicles subject but needs some modifications before considering for publications as follows:

-Abstract is poor,it must put in more details to display the new results added and the comparison between the present investigation and the previous.

-Keywords only 3, it must at least 6 words. (Done)

-In introduction section, all cited refs must indicate to the authors as example: "fossil-fuel-powered 26 vehicles[1]." must modify to "fossil-fuel-powered 26 vehicles discussed by Alanazi [1]" and so on. (Done)

-All symbols used in Equations must ensure inserted as nomenclature.

-Figure 6. Electrical component flowchart needs more details. (Done)

-Figure 8 doesn't clear, it needs more resolution. (Done)

-It is too interested if authors make simulation between the results obtained and the previous obtained by others.

-Conclusion must put in more details.

-Refs. list needs more recently references in the MS field.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The author has addressed the comments satisfactorily. However, I am not fully satisfied with the final version of the manuscript. The revisions appear to be more in line with a minor revision rather than the major revision that was required. I kindly request a more careful and thorough implementation of all the comments into the manuscript.

Author Response

Dear Sir,

In this paper the scope of data acquisition is mainly from the load cell sensor. So that the maximum results obtained as stated in the paper. For other data results are prepared for the next paper which will be submitted to this journal as well. Hopefully the current results are sufficient and worthy of publication. But apart from that, I have added an explanation according to your direction. thank you, this is quite valuable.

Thank you for your suggestion.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The paper can be accepted. However, the author must provide additional physical photos.

Author Response

Dear Sir,
Thank you for your input. I have accommodated some of your notes and added some pictures during the data collection process.

Author Response File: Author Response.docx