Performance comparison in Gen
Client
ABES Engineering College
Duration
12 weeks
Category
Data Science
My Approach: Performance Difference in BS4 & BS6 Generation of KTM RC 390
In an era of rapidly evolving emission standards, my project focuses on a compelling case study: the KTM RC390 motorcycle, specifically the 2017 BS4 variant and the 2020 BS6 variant. These variants represent two distinct stages of technological evolution in the automotive industry, emphasizing the importance of understanding their performance differences.
Emission standards, such as the transition from BS4 to BS6, necessitate changes in engine management systems and emissions control technologies. These changes impact not only environmental compliance but also motorcycle performance, fuel efficiency, and overall user experience. As these motorcycles cater to a discerning and passionate consumer base, transparency and informed decision-making are paramount.
My project leverages Engine Control Unit (ECU) insights and data sets to provide a data-driven analysis of these performance disparities. By delving into power output, fuel efficiency, emissions, and throttle response, I aim to uncover the intricate relationships between technology, regulation, and motorcycle performance. This research is crucial for consumers making purchasing decisions, manufacturers adapting to emission standards, and regulators evaluating the effectiveness of such standards.
Proposed Model
The proposed model for my research project is designed to comprehensively analyze and quantify the performance differences between the KTM RC390 2017 BS4 variant and the KTM RC390 2020 BS6 variant, with a specific emphasis on the influence of Engine Control Unit (ECU) configurations. To achieve this, I have outlined a systematic and data-driven approach with specific goals aimed at achieving a thorough understanding of the variations in power output, fuel efficiency, emissions, and throttle response between the two motorcycle models.
Data Collection: My research will commence with the meticulous collection of comprehensive ECU datasets from both the 2017 BS4 and 2020 BS6 variants of the KTM RC390. This data will serve as the foundational element of my analysis and will be pivotal in uncovering the nuances in ECU configurations.
Performance Metrics: To assess the performance differences, I will define and prioritize key performance metrics, including power output, fuel efficiency, emissions, and throttle response. These metrics will serve as the benchmark for my comparative analysis, providing a focused lens through which I can gauge the impact of ECU configurations on overall motorcycle performance.
Data Analysis: Employing advanced data analysis tools and methodologies, including statistical analysis and machine learning techniques, I will process and analyze the collected ECU data. This step is crucial in extracting meaningful insights and patterns that might not be immediately apparent through traditional analysis methods.
Visualization: To enhance the interpretability and accessibility of our findings, I will create informative and visually engaging representations of the performance differences between the 2017 BS4 and 2020 BS6 variants. These visualizations will include charts, graphs, and other graphical elements to effectively communicate complex data patterns.
Web Page Development: In line with my objective to make our research outcomes widely accessible, I will develop a dedicated web page to host the project's results. This web page will feature interactive visualizations, comprehensive reports, and a user-friendly interface, making it an ideal platform for stakeholders, enthusiasts, and consumers to explore our analysis. This initiative aligns with our commitment to transparency and knowledge dissemination in the context of the motorcycle industry's response to evolving regulatory landscapes.
By implementing this proposed model, I aim to provide stakeholders with valuable insights into the performance disparities resulting from technological advancements and changing emission standards in the motorcycle industry. Through a data-centric approach, our research seeks to offer a transparent and accessible resource for those interested in understanding how the KTM RC390 models have adapted to evolving regulatory landscapes.
Method of Making
My project begins with comprehensive data collection. We extract ECU insights and datasets from both variants, ensuring that we capture a wide range of parameters that influence motorcycle performance. Data preprocessing is a crucial step where we address data quality issues, remove outliers, and handle missing values to ensure the integrity and accuracy of our dataset.
A. Data Science
Python: We utilize Python as our primary programming language due to its versatility in data analysis, machine learning, and visualization. Libraries like NumPy, Pandas, Matplotlib, Seaborn, and Plotly are employed for data manipulation and visualization.
Machine Learning Frameworks: Scikit-Learn and TensorFlow are essential for model development and training. These frameworks offer a wide range of tools for regression analysis and deep learning, ensuring the accuracy of our predictions.
B. Web Development
Web Development Tools: For web-page development, we use HTML, CSS, and JavaScript to create a user-friendly interface. Frameworks like Flask or Django are considered for back-end development to serve the web-page.
C. Hardware Systems
Computer Workstations: High-performance workstations with multi-core processors and ample RAM are essential for data preprocessing, modeling, and visualization tasks.
Storage: Sufficient storage capacity is required to store and manage large datasets, model checkpoints, and web development files.
Graphics Processing Unit (GPU): Depending on the complexity of our machine learning models, we may utilize GPUs to accelerate model training, particularly for deep learning approaches.
D. Performance Calculation
The Brake Horsepower (BHP) of a motorcycle is a measure of the engine's power output. It represents the amount of power the engine produces before losses due to friction, heat, and other factors. The formula to calculate Brake Horsepower is:
BHP = T× N / constant
Where:
T is the torque produced by the engine in pound-feet (lbft).
N is the engine speed or RPM (revolutions per minute).
The constant is a conversion factor that depends on the units used for torque and RPM. If torque is measured in pound-feet and RPM in revolutions per minute, the constant is typically 5252.
Measure Torque: Torque is typically measured using a dynamometer (dyno). It is the force that causes an object to rotate around an axis, and in the context of an engine, it's the twisting force produced by the engine's crankshaft.
Measure RPM: The engine speed is measured in revolutions per minute (RPM). This can be obtained from the motorcycle's tachometer or directly from the dynamometer during testing. Calculate BHP: Use the formula mentioned above to calculate Brake Horsepower.
Results / Outcomes
The expected outcomes of our project are multifaceted, aiming to provide valuable insights into the performance differences between the KTM RC390 2017 BS4 and KTM RC390 2020 BS6 variants. The data visualization between the data came out successful with a accuracy of 96.31% in comparison of the verified data from the KTM Orange Factory, Mattighofen, Austria. The performance difference between both the generation of the motorcycle is significantly small but noticeable using the data visualization of BHP data of both the motorcycles.
Fig: KTM RC90 GEN 1 BS4 - BHP Data
Fig: KTM RC90 GEN 2 BS6 - BHP Data
I anticipate the creation of compelling visualizations that will effectively illustrate these performance differences. These visual representations will make complex data accessible to a broad audience, aiding enthusiasts, buyers, and industry stakeholders in their decision-making processes. The data is verified through the currently available data of the vehicle with similar specifications. Both the motorcycles are measured on same parameter and the data is plotted along with Torque and BHP records from 1000 RPM to maximum of 7000 RPM. In the process of obtaining the data a total of 17 steps of dynamo runs are performed for both the vehicles resulting is a data visualization of same data matrices. One of the key performance metrics we focused on in our comparative analysis of the KTM RC390 GEN1 BS4 and KTM RC390 GEN2 BS6 variants is Brake Horse Power (BHP). The line chart presented below vividly illustrates the BHP values for both motorcycle generations, providing a clear visual depiction of how the models compare in terms of this crucial performance parameter.
Fig:- Performance comparison between BS4 & BS6 KTM RC 390 motorcycle
My research yielded a comprehensive understanding of how changes in ECU configurations and the transition from BS4 to BS6 emission standards impact performance metrics of Break Horse Power (BHP).
Conclusions
The conclusions of my project findings are extensive and impactful, encompassing various stakeholders within the motorcycle industry and beyond:
Consumer Decision-Making: Motorcycle enthusiasts and potential buyers can make informed choices based on data- driven performance insights. They will have access to valuable information on how the two variants of the KTM RC390 perform under different conditions, assisting them in selecting the motorcycle that best suits their preferences and requirements.
Manufacturer Guidance: Motorcycle manufacturers can utilize our research to inform their design and engineering decisions. The performance data and insights we provide can guide them in adapting their models to meet evolving emission standards while optimizing key performance metrics.
Regulatory Evaluation: Regulatory bodies and environmental agencies can assess the effectiveness of emission standards by examining the real-world impact on motorcycle performance. Our analysis can contribute to discussions surrounding the balance between environmental regulations and vehicle performance.
Academic Research: Our project's methodology and findings can serve as a valuable resource for academic researchers and scholars interested in motorcycle performance, emission standards, and the role of ECUs in vehicle dynamics. It provides a foundation for further research in this field.
Knowledge Dissemination: The knowledge and insights gained from our project will be disseminated through academic publications, conference presentations, and potential collaborations. This ensures that our research reaches a broader audience, contributes to the academic discourse, and informs industry practices.
Performance comparison in Gen
Client
ABES Engineering College
Duration
12 weeks
Category
Data Science
My Approach: Performance Difference in BS4 & BS6 Generation of KTM RC 390
In an era of rapidly evolving emission standards, my project focuses on a compelling case study: the KTM RC390 motorcycle, specifically the 2017 BS4 variant and the 2020 BS6 variant. These variants represent two distinct stages of technological evolution in the automotive industry, emphasizing the importance of understanding their performance differences.
Emission standards, such as the transition from BS4 to BS6, necessitate changes in engine management systems and emissions control technologies. These changes impact not only environmental compliance but also motorcycle performance, fuel efficiency, and overall user experience. As these motorcycles cater to a discerning and passionate consumer base, transparency and informed decision-making are paramount.
My project leverages Engine Control Unit (ECU) insights and data sets to provide a data-driven analysis of these performance disparities. By delving into power output, fuel efficiency, emissions, and throttle response, I aim to uncover the intricate relationships between technology, regulation, and motorcycle performance. This research is crucial for consumers making purchasing decisions, manufacturers adapting to emission standards, and regulators evaluating the effectiveness of such standards.
Proposed Model
The proposed model for my research project is designed to comprehensively analyze and quantify the performance differences between the KTM RC390 2017 BS4 variant and the KTM RC390 2020 BS6 variant, with a specific emphasis on the influence of Engine Control Unit (ECU) configurations. To achieve this, I have outlined a systematic and data-driven approach with specific goals aimed at achieving a thorough understanding of the variations in power output, fuel efficiency, emissions, and throttle response between the two motorcycle models.
Data Collection: My research will commence with the meticulous collection of comprehensive ECU datasets from both the 2017 BS4 and 2020 BS6 variants of the KTM RC390. This data will serve as the foundational element of my analysis and will be pivotal in uncovering the nuances in ECU configurations.
Performance Metrics: To assess the performance differences, I will define and prioritize key performance metrics, including power output, fuel efficiency, emissions, and throttle response. These metrics will serve as the benchmark for my comparative analysis, providing a focused lens through which I can gauge the impact of ECU configurations on overall motorcycle performance.
Data Analysis: Employing advanced data analysis tools and methodologies, including statistical analysis and machine learning techniques, I will process and analyze the collected ECU data. This step is crucial in extracting meaningful insights and patterns that might not be immediately apparent through traditional analysis methods.
Visualization: To enhance the interpretability and accessibility of our findings, I will create informative and visually engaging representations of the performance differences between the 2017 BS4 and 2020 BS6 variants. These visualizations will include charts, graphs, and other graphical elements to effectively communicate complex data patterns.
Web Page Development: In line with my objective to make our research outcomes widely accessible, I will develop a dedicated web page to host the project's results. This web page will feature interactive visualizations, comprehensive reports, and a user-friendly interface, making it an ideal platform for stakeholders, enthusiasts, and consumers to explore our analysis. This initiative aligns with our commitment to transparency and knowledge dissemination in the context of the motorcycle industry's response to evolving regulatory landscapes.
By implementing this proposed model, I aim to provide stakeholders with valuable insights into the performance disparities resulting from technological advancements and changing emission standards in the motorcycle industry. Through a data-centric approach, our research seeks to offer a transparent and accessible resource for those interested in understanding how the KTM RC390 models have adapted to evolving regulatory landscapes.
Method of Making
My project begins with comprehensive data collection. We extract ECU insights and datasets from both variants, ensuring that we capture a wide range of parameters that influence motorcycle performance. Data preprocessing is a crucial step where we address data quality issues, remove outliers, and handle missing values to ensure the integrity and accuracy of our dataset.
A. Data Science
Python: We utilize Python as our primary programming language due to its versatility in data analysis, machine learning, and visualization. Libraries like NumPy, Pandas, Matplotlib, Seaborn, and Plotly are employed for data manipulation and visualization.
Machine Learning Frameworks: Scikit-Learn and TensorFlow are essential for model development and training. These frameworks offer a wide range of tools for regression analysis and deep learning, ensuring the accuracy of our predictions.
B. Web Development
Web Development Tools: For web-page development, we use HTML, CSS, and JavaScript to create a user-friendly interface. Frameworks like Flask or Django are considered for back-end development to serve the web-page.
C. Hardware Systems
Computer Workstations: High-performance workstations with multi-core processors and ample RAM are essential for data preprocessing, modeling, and visualization tasks.
Storage: Sufficient storage capacity is required to store and manage large datasets, model checkpoints, and web development files.
Graphics Processing Unit (GPU): Depending on the complexity of our machine learning models, we may utilize GPUs to accelerate model training, particularly for deep learning approaches.
D. Performance Calculation
The Brake Horsepower (BHP) of a motorcycle is a measure of the engine's power output. It represents the amount of power the engine produces before losses due to friction, heat, and other factors. The formula to calculate Brake Horsepower is:
BHP = T× N / constant
Where:
T is the torque produced by the engine in pound-feet (lbft).
N is the engine speed or RPM (revolutions per minute).
The constant is a conversion factor that depends on the units used for torque and RPM. If torque is measured in pound-feet and RPM in revolutions per minute, the constant is typically 5252.
Measure Torque: Torque is typically measured using a dynamometer (dyno). It is the force that causes an object to rotate around an axis, and in the context of an engine, it's the twisting force produced by the engine's crankshaft.
Measure RPM: The engine speed is measured in revolutions per minute (RPM). This can be obtained from the motorcycle's tachometer or directly from the dynamometer during testing. Calculate BHP: Use the formula mentioned above to calculate Brake Horsepower.
Results / Outcomes
The expected outcomes of our project are multifaceted, aiming to provide valuable insights into the performance differences between the KTM RC390 2017 BS4 and KTM RC390 2020 BS6 variants. The data visualization between the data came out successful with a accuracy of 96.31% in comparison of the verified data from the KTM Orange Factory, Mattighofen, Austria. The performance difference between both the generation of the motorcycle is significantly small but noticeable using the data visualization of BHP data of both the motorcycles.
Fig: KTM RC90 GEN 1 BS4 - BHP Data
Fig: KTM RC90 GEN 2 BS6 - BHP Data
I anticipate the creation of compelling visualizations that will effectively illustrate these performance differences. These visual representations will make complex data accessible to a broad audience, aiding enthusiasts, buyers, and industry stakeholders in their decision-making processes. The data is verified through the currently available data of the vehicle with similar specifications. Both the motorcycles are measured on same parameter and the data is plotted along with Torque and BHP records from 1000 RPM to maximum of 7000 RPM. In the process of obtaining the data a total of 17 steps of dynamo runs are performed for both the vehicles resulting is a data visualization of same data matrices. One of the key performance metrics we focused on in our comparative analysis of the KTM RC390 GEN1 BS4 and KTM RC390 GEN2 BS6 variants is Brake Horse Power (BHP). The line chart presented below vividly illustrates the BHP values for both motorcycle generations, providing a clear visual depiction of how the models compare in terms of this crucial performance parameter.
Fig:- Performance comparison between BS4 & BS6 KTM RC 390 motorcycle
My research yielded a comprehensive understanding of how changes in ECU configurations and the transition from BS4 to BS6 emission standards impact performance metrics of Break Horse Power (BHP).
Conclusions
The conclusions of my project findings are extensive and impactful, encompassing various stakeholders within the motorcycle industry and beyond:
Consumer Decision-Making: Motorcycle enthusiasts and potential buyers can make informed choices based on data- driven performance insights. They will have access to valuable information on how the two variants of the KTM RC390 perform under different conditions, assisting them in selecting the motorcycle that best suits their preferences and requirements.
Manufacturer Guidance: Motorcycle manufacturers can utilize our research to inform their design and engineering decisions. The performance data and insights we provide can guide them in adapting their models to meet evolving emission standards while optimizing key performance metrics.
Regulatory Evaluation: Regulatory bodies and environmental agencies can assess the effectiveness of emission standards by examining the real-world impact on motorcycle performance. Our analysis can contribute to discussions surrounding the balance between environmental regulations and vehicle performance.
Academic Research: Our project's methodology and findings can serve as a valuable resource for academic researchers and scholars interested in motorcycle performance, emission standards, and the role of ECUs in vehicle dynamics. It provides a foundation for further research in this field.
Knowledge Dissemination: The knowledge and insights gained from our project will be disseminated through academic publications, conference presentations, and potential collaborations. This ensures that our research reaches a broader audience, contributes to the academic discourse, and informs industry practices.
Performance comparison in Gen
ABES Engineering College
12 weeks
Data Science
My Approach: Performance Difference in BS4 & BS6 Generation of KTM RC 390
In an era of rapidly evolving emission standards, my project focuses on a compelling case study: the KTM RC390 motorcycle, specifically the 2017 BS4 variant and the 2020 BS6 variant. These variants represent two distinct stages of technological evolution in the automotive industry, emphasizing the importance of understanding their performance differences.
Emission standards, such as the transition from BS4 to BS6, necessitate changes in engine management systems and emissions control technologies. These changes impact not only environmental compliance but also motorcycle performance, fuel efficiency, and overall user experience. As these motorcycles cater to a discerning and passionate consumer base, transparency and informed decision-making are paramount.
My project leverages Engine Control Unit (ECU) insights and data sets to provide a data-driven analysis of these performance disparities. By delving into power output, fuel efficiency, emissions, and throttle response, I aim to uncover the intricate relationships between technology, regulation, and motorcycle performance. This research is crucial for consumers making purchasing decisions, manufacturers adapting to emission standards, and regulators evaluating the effectiveness of such standards.
Proposed Model
The proposed model for my research project is designed to comprehensively analyze and quantify the performance differences between the KTM RC390 2017 BS4 variant and the KTM RC390 2020 BS6 variant, with a specific emphasis on the influence of Engine Control Unit (ECU) configurations. To achieve this, I have outlined a systematic and data-driven approach with specific goals aimed at achieving a thorough understanding of the variations in power output, fuel efficiency, emissions, and throttle response between the two motorcycle models.
Data Collection: My research will commence with the meticulous collection of comprehensive ECU datasets from both the 2017 BS4 and 2020 BS6 variants of the KTM RC390. This data will serve as the foundational element of my analysis and will be pivotal in uncovering the nuances in ECU configurations.
Performance Metrics: To assess the performance differences, I will define and prioritize key performance metrics, including power output, fuel efficiency, emissions, and throttle response. These metrics will serve as the benchmark for my comparative analysis, providing a focused lens through which I can gauge the impact of ECU configurations on overall motorcycle performance.
Data Analysis: Employing advanced data analysis tools and methodologies, including statistical analysis and machine learning techniques, I will process and analyze the collected ECU data. This step is crucial in extracting meaningful insights and patterns that might not be immediately apparent through traditional analysis methods.
Visualization: To enhance the interpretability and accessibility of our findings, I will create informative and visually engaging representations of the performance differences between the 2017 BS4 and 2020 BS6 variants. These visualizations will include charts, graphs, and other graphical elements to effectively communicate complex data patterns.
Web Page Development: In line with my objective to make our research outcomes widely accessible, I will develop a dedicated web page to host the project's results. This web page will feature interactive visualizations, comprehensive reports, and a user-friendly interface, making it an ideal platform for stakeholders, enthusiasts, and consumers to explore our analysis. This initiative aligns with our commitment to transparency and knowledge dissemination in the context of the motorcycle industry's response to evolving regulatory landscapes.
By implementing this proposed model, I aim to provide stakeholders with valuable insights into the performance disparities resulting from technological advancements and changing emission standards in the motorcycle industry. Through a data-centric approach, our research seeks to offer a transparent and accessible resource for those interested in understanding how the KTM RC390 models have adapted to evolving regulatory landscapes.
Method of Making
My project begins with comprehensive data collection. We extract ECU insights and datasets from both variants, ensuring that we capture a wide range of parameters that influence motorcycle performance. Data preprocessing is a crucial step where we address data quality issues, remove outliers, and handle missing values to ensure the integrity and accuracy of our dataset.
A. Data Science
Python: We utilize Python as our primary programming language due to its versatility in data analysis, machine learning, and visualization. Libraries like NumPy, Pandas, Matplotlib, Seaborn, and Plotly are employed for data manipulation and visualization.
Machine Learning Frameworks: Scikit-Learn and TensorFlow are essential for model development and training. These frameworks offer a wide range of tools for regression analysis and deep learning, ensuring the accuracy of our predictions.
B. Web Development
Web Development Tools: For web-page development, we use HTML, CSS, and JavaScript to create a user-friendly interface. Frameworks like Flask or Django are considered for back-end development to serve the web-page.
C. Hardware Systems
Computer Workstations: High-performance workstations with multi-core processors and ample RAM are essential for data preprocessing, modeling, and visualization tasks.
Storage: Sufficient storage capacity is required to store and manage large datasets, model checkpoints, and web development files.
Graphics Processing Unit (GPU): Depending on the complexity of our machine learning models, we may utilize GPUs to accelerate model training, particularly for deep learning approaches.
D. Performance Calculation
The Brake Horsepower (BHP) of a motorcycle is a measure of the engine's power output. It represents the amount of power the engine produces before losses due to friction, heat, and other factors. The formula to calculate Brake Horsepower is:
BHP = T× N / constant
Where:
T is the torque produced by the engine in pound-feet (lbft).
N is the engine speed or RPM (revolutions per minute).
The constant is a conversion factor that depends on the units used for torque and RPM. If torque is measured in pound-feet and RPM in revolutions per minute, the constant is typically 5252.
Measure Torque: Torque is typically measured using a dynamometer (dyno). It is the force that causes an object to rotate around an axis, and in the context of an engine, it's the twisting force produced by the engine's crankshaft.
Measure RPM: The engine speed is measured in revolutions per minute (RPM). This can be obtained from the motorcycle's tachometer or directly from the dynamometer during testing. Calculate BHP: Use the formula mentioned above to calculate Brake Horsepower.
Results / Outcomes
The expected outcomes of our project are multifaceted, aiming to provide valuable insights into the performance differences between the KTM RC390 2017 BS4 and KTM RC390 2020 BS6 variants. The data visualization between the data came out successful with a accuracy of 96.31% in comparison of the verified data from the KTM Orange Factory, Mattighofen, Austria. The performance difference between both the generation of the motorcycle is significantly small but noticeable using the data visualization of BHP data of both the motorcycles.
Fig: KTM RC90 GEN 1 BS4 - BHP Data
Fig: KTM RC90 GEN 2 BS6 - BHP Data
I anticipate the creation of compelling visualizations that will effectively illustrate these performance differences. These visual representations will make complex data accessible to a broad audience, aiding enthusiasts, buyers, and industry stakeholders in their decision-making processes. The data is verified through the currently available data of the vehicle with similar specifications. Both the motorcycles are measured on same parameter and the data is plotted along with Torque and BHP records from 1000 RPM to maximum of 7000 RPM. In the process of obtaining the data a total of 17 steps of dynamo runs are performed for both the vehicles resulting is a data visualization of same data matrices. One of the key performance metrics we focused on in our comparative analysis of the KTM RC390 GEN1 BS4 and KTM RC390 GEN2 BS6 variants is Brake Horse Power (BHP). The line chart presented below vividly illustrates the BHP values for both motorcycle generations, providing a clear visual depiction of how the models compare in terms of this crucial performance parameter.
Fig:- Performance comparison between BS4 & BS6 KTM RC 390 motorcycle
My research yielded a comprehensive understanding of how changes in ECU configurations and the transition from BS4 to BS6 emission standards impact performance metrics of Break Horse Power (BHP).
Conclusions
The conclusions of my project findings are extensive and impactful, encompassing various stakeholders within the motorcycle industry and beyond:
Consumer Decision-Making: Motorcycle enthusiasts and potential buyers can make informed choices based on data- driven performance insights. They will have access to valuable information on how the two variants of the KTM RC390 perform under different conditions, assisting them in selecting the motorcycle that best suits their preferences and requirements.
Manufacturer Guidance: Motorcycle manufacturers can utilize our research to inform their design and engineering decisions. The performance data and insights we provide can guide them in adapting their models to meet evolving emission standards while optimizing key performance metrics.
Regulatory Evaluation: Regulatory bodies and environmental agencies can assess the effectiveness of emission standards by examining the real-world impact on motorcycle performance. Our analysis can contribute to discussions surrounding the balance between environmental regulations and vehicle performance.
Academic Research: Our project's methodology and findings can serve as a valuable resource for academic researchers and scholars interested in motorcycle performance, emission standards, and the role of ECUs in vehicle dynamics. It provides a foundation for further research in this field.
Knowledge Dissemination: The knowledge and insights gained from our project will be disseminated through academic publications, conference presentations, and potential collaborations. This ensures that our research reaches a broader audience, contributes to the academic discourse, and informs industry practices.
Performance comparison in Gen
Client
ABES Engineering College
Duration
12 weeks
Category
Data Science
My Approach: Performance Difference in BS4 & BS6 Generation of KTM RC 390
In an era of rapidly evolving emission standards, my project focuses on a compelling case study: the KTM RC390 motorcycle, specifically the 2017 BS4 variant and the 2020 BS6 variant. These variants represent two distinct stages of technological evolution in the automotive industry, emphasizing the importance of understanding their performance differences.
Emission standards, such as the transition from BS4 to BS6, necessitate changes in engine management systems and emissions control technologies. These changes impact not only environmental compliance but also motorcycle performance, fuel efficiency, and overall user experience. As these motorcycles cater to a discerning and passionate consumer base, transparency and informed decision-making are paramount.
My project leverages Engine Control Unit (ECU) insights and data sets to provide a data-driven analysis of these performance disparities. By delving into power output, fuel efficiency, emissions, and throttle response, I aim to uncover the intricate relationships between technology, regulation, and motorcycle performance. This research is crucial for consumers making purchasing decisions, manufacturers adapting to emission standards, and regulators evaluating the effectiveness of such standards.
Proposed Model
The proposed model for my research project is designed to comprehensively analyze and quantify the performance differences between the KTM RC390 2017 BS4 variant and the KTM RC390 2020 BS6 variant, with a specific emphasis on the influence of Engine Control Unit (ECU) configurations. To achieve this, I have outlined a systematic and data-driven approach with specific goals aimed at achieving a thorough understanding of the variations in power output, fuel efficiency, emissions, and throttle response between the two motorcycle models.
Data Collection: My research will commence with the meticulous collection of comprehensive ECU datasets from both the 2017 BS4 and 2020 BS6 variants of the KTM RC390. This data will serve as the foundational element of my analysis and will be pivotal in uncovering the nuances in ECU configurations.
Performance Metrics: To assess the performance differences, I will define and prioritize key performance metrics, including power output, fuel efficiency, emissions, and throttle response. These metrics will serve as the benchmark for my comparative analysis, providing a focused lens through which I can gauge the impact of ECU configurations on overall motorcycle performance.
Data Analysis: Employing advanced data analysis tools and methodologies, including statistical analysis and machine learning techniques, I will process and analyze the collected ECU data. This step is crucial in extracting meaningful insights and patterns that might not be immediately apparent through traditional analysis methods.
Visualization: To enhance the interpretability and accessibility of our findings, I will create informative and visually engaging representations of the performance differences between the 2017 BS4 and 2020 BS6 variants. These visualizations will include charts, graphs, and other graphical elements to effectively communicate complex data patterns.
Web Page Development: In line with my objective to make our research outcomes widely accessible, I will develop a dedicated web page to host the project's results. This web page will feature interactive visualizations, comprehensive reports, and a user-friendly interface, making it an ideal platform for stakeholders, enthusiasts, and consumers to explore our analysis. This initiative aligns with our commitment to transparency and knowledge dissemination in the context of the motorcycle industry's response to evolving regulatory landscapes.
By implementing this proposed model, I aim to provide stakeholders with valuable insights into the performance disparities resulting from technological advancements and changing emission standards in the motorcycle industry. Through a data-centric approach, our research seeks to offer a transparent and accessible resource for those interested in understanding how the KTM RC390 models have adapted to evolving regulatory landscapes.
Method of Making
My project begins with comprehensive data collection. We extract ECU insights and datasets from both variants, ensuring that we capture a wide range of parameters that influence motorcycle performance. Data preprocessing is a crucial step where we address data quality issues, remove outliers, and handle missing values to ensure the integrity and accuracy of our dataset.
A. Data Science
Python: We utilize Python as our primary programming language due to its versatility in data analysis, machine learning, and visualization. Libraries like NumPy, Pandas, Matplotlib, Seaborn, and Plotly are employed for data manipulation and visualization.
Machine Learning Frameworks: Scikit-Learn and TensorFlow are essential for model development and training. These frameworks offer a wide range of tools for regression analysis and deep learning, ensuring the accuracy of our predictions.
B. Web Development
Web Development Tools: For web-page development, we use HTML, CSS, and JavaScript to create a user-friendly interface. Frameworks like Flask or Django are considered for back-end development to serve the web-page.
C. Hardware Systems
Computer Workstations: High-performance workstations with multi-core processors and ample RAM are essential for data preprocessing, modeling, and visualization tasks.
Storage: Sufficient storage capacity is required to store and manage large datasets, model checkpoints, and web development files.
Graphics Processing Unit (GPU): Depending on the complexity of our machine learning models, we may utilize GPUs to accelerate model training, particularly for deep learning approaches.
D. Performance Calculation
The Brake Horsepower (BHP) of a motorcycle is a measure of the engine's power output. It represents the amount of power the engine produces before losses due to friction, heat, and other factors. The formula to calculate Brake Horsepower is:
BHP = T× N / constant
Where:
T is the torque produced by the engine in pound-feet (lbft).
N is the engine speed or RPM (revolutions per minute).
The constant is a conversion factor that depends on the units used for torque and RPM. If torque is measured in pound-feet and RPM in revolutions per minute, the constant is typically 5252.
Measure Torque: Torque is typically measured using a dynamometer (dyno). It is the force that causes an object to rotate around an axis, and in the context of an engine, it's the twisting force produced by the engine's crankshaft.
Measure RPM: The engine speed is measured in revolutions per minute (RPM). This can be obtained from the motorcycle's tachometer or directly from the dynamometer during testing. Calculate BHP: Use the formula mentioned above to calculate Brake Horsepower.
Results / Outcomes
The expected outcomes of our project are multifaceted, aiming to provide valuable insights into the performance differences between the KTM RC390 2017 BS4 and KTM RC390 2020 BS6 variants. The data visualization between the data came out successful with a accuracy of 96.31% in comparison of the verified data from the KTM Orange Factory, Mattighofen, Austria. The performance difference between both the generation of the motorcycle is significantly small but noticeable using the data visualization of BHP data of both the motorcycles.
Fig: KTM RC90 GEN 1 BS4 - BHP Data
Fig: KTM RC90 GEN 2 BS6 - BHP Data
I anticipate the creation of compelling visualizations that will effectively illustrate these performance differences. These visual representations will make complex data accessible to a broad audience, aiding enthusiasts, buyers, and industry stakeholders in their decision-making processes. The data is verified through the currently available data of the vehicle with similar specifications. Both the motorcycles are measured on same parameter and the data is plotted along with Torque and BHP records from 1000 RPM to maximum of 7000 RPM. In the process of obtaining the data a total of 17 steps of dynamo runs are performed for both the vehicles resulting is a data visualization of same data matrices. One of the key performance metrics we focused on in our comparative analysis of the KTM RC390 GEN1 BS4 and KTM RC390 GEN2 BS6 variants is Brake Horse Power (BHP). The line chart presented below vividly illustrates the BHP values for both motorcycle generations, providing a clear visual depiction of how the models compare in terms of this crucial performance parameter.
Fig:- Performance comparison between BS4 & BS6 KTM RC 390 motorcycle
My research yielded a comprehensive understanding of how changes in ECU configurations and the transition from BS4 to BS6 emission standards impact performance metrics of Break Horse Power (BHP).
Conclusions
The conclusions of my project findings are extensive and impactful, encompassing various stakeholders within the motorcycle industry and beyond:
Consumer Decision-Making: Motorcycle enthusiasts and potential buyers can make informed choices based on data- driven performance insights. They will have access to valuable information on how the two variants of the KTM RC390 perform under different conditions, assisting them in selecting the motorcycle that best suits their preferences and requirements.
Manufacturer Guidance: Motorcycle manufacturers can utilize our research to inform their design and engineering decisions. The performance data and insights we provide can guide them in adapting their models to meet evolving emission standards while optimizing key performance metrics.
Regulatory Evaluation: Regulatory bodies and environmental agencies can assess the effectiveness of emission standards by examining the real-world impact on motorcycle performance. Our analysis can contribute to discussions surrounding the balance between environmental regulations and vehicle performance.
Academic Research: Our project's methodology and findings can serve as a valuable resource for academic researchers and scholars interested in motorcycle performance, emission standards, and the role of ECUs in vehicle dynamics. It provides a foundation for further research in this field.
Knowledge Dissemination: The knowledge and insights gained from our project will be disseminated through academic publications, conference presentations, and potential collaborations. This ensures that our research reaches a broader audience, contributes to the academic discourse, and informs industry practices.
