Analyzing Vehicle Performance For Petrol, hybrid And.pptx

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1. Analyzing Vehicle Performance For Petrol, hybrid and Electric Cars Using MATLAB Supervisor:   Mr.Jagdeep Kumar   Assistant Professor Submitted By: Sunil Kumar Sharma   Enrolment No:   19E2SGREM40P607 Department of Mechanical Engineering   Sobhasaria Group of Institutions, Sikar   Bikaner Technical University, Bikaner (Rajasthan)

2. Abstract This research explores the transformation within the automotive sector, driven by diverse vehicle propulsion technologies such as petrol, hybrid, and electric powertrains. Utilizing MATLAB, the study delves into a comprehensive analysis of engine and motor torque characteristics, the impact of road resistance, vehicle performance metrics, braking dynamics, and driving range simulations. MATLAB is employed to model road resistance, enabling a deeper understanding of power performance. Critical parameters like maximum speed, acceleration time, gradeability, and electric vehicle range are computed. The research also focuses on braking performance, specifically the Anti-Lock Braking System (ABS), and predicts vehicle performance under various driving conditions. The study emphasizes electric vehicles, including Battery Electric Vehicles (BEVs) and Extended Range Electric Vehicles (E-REVs), utilizing MATLAB simulations for power and battery parameter matching. For Fuel Cell Electric Vehicles (FCEVs), mathematical modeling and parameter matching are employed for an efficient power system. The research concludes by combining theoretical understanding with practical application through MATLAB, providing a robust framework for improving vehicle design and operation in the evolving transportation landscape.

3. introduction At the start of the 20th century, internal combustion engines (ICEs) powered by gasoline and diesel replaced horse-drawn carriages and steam vehicles, revolutionizing personal transportation and industries. In the early 20th century, mass production techniques, notably Henry Ford's assembly line, made cars more accessible, transforming them from luxuries to essential modes of transportation for people of diverse backgrounds. Throughout the mid-20th century, engineering and technological advancements, including automatic transmissions and aerodynamics, significantly improved vehicle performance, driving experience, and fuel efficiency. In the latter half of the 20th century, environmental concerns, highlighted by the 1970s oil crisis, led to the development of hybrid vehicles combining internal combustion engines with electric motors for improved fuel efficiency and reduced emissions. In the 21st century, advancements in battery technology led to practical electric vehicles (EVs) with longer ranges and faster charging. Governments and industries invested in EV growth to address climate change and air quality concerns, prompting stricter emissions regulations. The future of vehicle propulsion involves integrating and optimizing diverse technologies like hybrids, plug-in hybrids, and battery electric vehicles. Concepts such as vehicle-to-grid integration and smart charging explore ways to make vehicles dynamic parts of the energy grid. The automotive industry continues to balance performance, efficiency, and environmental responsibility in its pursuit of evolving propulsion technologies.

4. Electric Vehicles Electric vehicles (EVs) mark a significant departure from the conventional internal combustion engine (ICE) vehicles, relying predominantly on electricity as their primary propulsion source. This category encompasses a variety of vehicle types that utilize electric motors for propulsion, thereby reducing or even eliminating the need for fossil fuels. Battery Electric Vehicles (BEVs) rely solely on electricity stored in high-capacity batteries to power an electric motor, emitting zero tailpipe emissions. Key features include zero emissions, intermittent charging from external sources, variable electric range based on battery capacity, reliance on charging infrastructure, and higher energy efficiency compared to internal combustion engine (ICE) vehicles, thanks to regenerative braking. Extended Range Electric Vehicles (E-REVs) combine electric and hybrid technologies, featuring an electric motor, a sizable battery for electric operation, and an auxiliary internal combustion engine as a range extender. Key features encompass emission-free electric driving within a set range, range extension through the internal combustion engine, reduced range anxiety, and versatility for longer journeys or areas with limited recharging infrastructure. Fuel Cell Electric Vehicles (FCEVs) utilize hydrogen fuel cells for electricity generation, emitting only water vapor. Key features include zero emissions, the need for a hydrogen refueling network, a driving range comparable to traditional vehicles, and efficient performance suitable for various applications, including long-distance travel and commercial use. Each vehicle type contributes to the evolving landscape of electric mobility, addressing environmental concerns and reducing reliance on fossil fuels. The research aims to assess and simulate the performance of these vehicles using tools like MATLAB, providing insights into their capabilities within the future of transporta

5. objectives The objectives of Dissertation are as follows, Objective 1: Characteristic Curve Analysis and Road Resistance Understanding Objective 2: Performance Metrics Calculation and Analysis Objective 3: Braking Performance and Stability Analysis Objective 4: Electric Vehicle Performance and Simulation Objective 5: Fuel Cell Electric Vehicle Analysis Objective 6: Parameter Matching and Design Optimization Objective 7: Simulation and Performance Prediction Through these defined objectives, the thesis aims to provide a comprehensive analysis of vehicle performance across different propulsion technologies, including petrol, hybrid, and electric vehicles. Utilizing MATLAB simulations and modeling, the research seeks to contribute to a deeper understanding of how various factors influence vehicle behavior, power requirements, and efficiency, ultimately shaping the design and operation of future vehicles in an evolving automotive landscape.

6. Problem statement The automotive industry is currently facing a critical juncture driven by environmental concerns, energy efficiency imperatives, and technological advancements. The shift from traditional internal combustion engine (ICE) vehicles to electric mobility, including Battery Electric Vehicles (BEVs), Extended Range Electric Vehicles (E-REVs), and Fuel Cell Electric Vehicles (FCEVs), presents both challenges and opportunities. To optimize these diverse propulsion systems, a thorough analysis of their performance characteristics, power requirements, efficiency, and interactions with external factors like road resistance and braking dynamics is essential. The practical implications of these technologies in real-world scenarios need careful investigation, considering the dynamic interplay between various vehicle parameters such as weight, aerodynamics, and tire characteristics. Additionally, the selection of components like motors, batteries, and fuel cells plays a crucial role in achieving optimal vehicle behavior and meeting design objectives. As the automotive landscape evolves, key questions must be addressed, including the performance of different propulsion technologies, the impact of external forces on power requirements, maximizing braking efficiency, understanding the influence of parameters on electric vehicle power performance, matching battery and motor parameters, and predicting vehicle performance through simulations. This research aims to fill these knowledge gaps by employing advanced simulation techniques and MATLAB modeling. By analyzing the interactions and performance of diverse propulsion technologies, the study seeks to offer valuable insights for designing, optimizing, and operating vehicles that align with the environmental and energy efficiency demands of the evolving transportation landscape.

7. Scope of research This research focuses on the in-depth analysis and simulation of vehicle performance across various propulsion technologies, with a primary emphasis on Battery Electric Vehicles (BEVs), Extended Range Electric Vehicles (E-REVs), and Fuel Cell Electric Vehicles (FCEVs). The scope includes the comprehensive exploration of their unique characteristics, interactions, and performance metrics such as power, acceleration, top speeds, driving ranges, and braking efficiency. MATLAB simulations are utilized to model propulsion systems, analyze performance dynamics, predict driving ranges, and simulate braking behavior under different conditions. The research also delves into parameter matching, design optimization, and explores the influence of different driving scenarios on vehicle behavior. While acknowledging the complexity of real-world systems, the study uses simplified models and theoretical assumptions, recognizing potential variations in data accuracy. In summary, this research aims to advance our understanding of vehicle performance within the evolving landscape of propulsion technologies, providing valuable insights for optimizing vehicle design, operation, and sustainability in the modern automotive era.

8. Literature review Author & Year Approach Main Points H. S. Pavan et al. (2022) Hybrid energy storage for PEVs Proposes a hybrid energy storage system using batteries and fuel cells for PEVs, managed by an Energy Management System (EMS). J. Dong and J. Bauman (2022) Fuel cell range extender for vehicles Introduces a fuel cell range extender vehicle (FCREV) concept optimizing driving range using fuel cell charging during driving and parking. G. Huang et al. (2022) Energy management strategy using DRL Suggests a deep reinforcement learning (DRL) based energy management strategy for EVs, optimizing efficiency considering battery thermal effects. P. Zheng and J. Bauman (2022) Solar and fuel cell integration Proposes a practical multi-port converter to integrate solar and fuel cell functions for EVs, validated through simulation. A. Ferrara and C. Hametner (2022) Eco-driving for fuel cell trucks Develops optimal speed plans using dynamic programming for fuel cell trucks, enhancing vehicle range by 8% compared to constant speed plans. B. Balasingam et al. (2022) Battery management systems Discusses the critical role of battery management systems (BMS) in various applications, including electric vehicles. F. El Bakkari et al. (2021) Electric vehicles and cleaner energy Explores the significance of electric cars, their recent developments, and challenges in energy storage for a low-carbon transition. M. Rasheed et al. (2021) Innovative energy architecture for EVs Presents a novel energy architecture combining energy-dense and power-dense batteries via a supercapacitor and DC-DC converter.

9. Research gaps Optimal Energy Management: Develop more advanced and adaptive energy management strategies for hybrid vehicles, incorporating machine learning or artificial intelligence to dynamically optimize energy distribution based on driving conditions, battery health, and real-time energy availability. Performance and Reliability: Conduct empirical field tests and real-world applications to assess the efficiency, reliability, long-term performance, degradation rates, and stability of hybrid systems under various conditions. Energy Storage Technologies: Investigate the most suitable combinations of energy storage technologies (e.g., Li-ion batteries, ultracapacitors, hydrogen fuel cells) for different vehicle types, routes, and driving patterns. Conduct comparative studies evaluating trade-offs in terms of efficiency, cost, weight, and lifespan. Range Extension and Charging Infrastructure: Explore optimal strategies for extending vehicle range while considering factors such as charging infrastructure availability, user convenience, and economic viability. Address challenges related to charging station placement, fast-charging technology, and grid impact for hybrid and electric vehicles. Eco-Driving and Sustainable Transportation: Develop more sophisticated and context-aware eco-driving algorithms for fuel cell vehicles and hybrid systems. Leverage vehicle characteristics, energy sources, and real-time traffic information to promote sustainable transportation systems.

10. Proposed approach 1. Initialize Simulation Parameters and Vehicle Characteristics: - Define parameters like vehicle mass, frontal area, rolling resistance coefficient, etc. - Set simulation time steps, driving cycle, and other relevant variables. 2. For each Propulsion Technology (BEV, E-REV, FCEV): 3. Develop Dynamic Simulation Model: - Define equations for power delivery, torque-speed relationships, and efficiency. - Incorporate battery models (state of charge, energy capacity) and motor characteristics. - Integrate relevant equations for each propulsion technology (BEV, E-REV, FCEV).

11. Proposed approach 5. Calculate Acceleration, Maximum Speed, and Driving Range: - Analyze simulation results to calculate acceleration time and maximum speed. - Determine driving range based on the energy consumption and battery capacity. 6. Analyze Power Requirements, Efficiency, and Energy Consumption: - Plot torque-speed curves and power output characteristics for each propulsion technology. - Calculate and analyze energy consumption and efficiency profiles. 7. Evaluate Braking Efficiency and Stability: - Implement braking algorithms to simulate regenerative and traditional braking. - Calculate braking distance, deceleration, and braking efficiency.

12. Proposed approach 8. Match Motor, Battery, or Fuel Cell Parameters for Optimization: - Iterate through combinations of motor, battery, or fuel cell parameters. - Simulate vehicle performance for each combination and evaluate metrics. 9. Simulate Impact of Parameters on Power Performance: - Alter specific parameters (e.g., battery capacity, motor power) and analyze effects. - Plot sensitivity analysis graphs to visualize parameter impacts. 10. Analyze Simulation Results and Collected Data: - Organize and analyze simulation output data, including acceleration times, range, efficiency, etc.

13. Proposed approach 11. Interpret Findings in Relation to Research Questions and Objectives: - Analyze the impact of different propulsion technologies on performance metrics. - Identify trends, correlations, and implications for vehicle design and operation. 12. Draw Conclusions Based on Analysis: - Summarize key findings related to power performance, efficiency, and vehicle dynamics. - Compare and contrast the performance of different propulsion technologies. 13. Provide Recommendations for Vehicle Design and Policy Formulation: - Recommend optimal combinations of components for specific vehicle requirements. - Suggest design modifications to improve efficiency and overall performance. 14. Identify Areas for Future Research: - Based on findings and limitations, identify areas for further investigation and refinement.

14. Proposed approach 15. End of Propulsion Technology Loop 16. Conclude Research: - Summarize overall findings, contributions, and implications of the study. - Highlight the significance of results for the automotive industry and sustainability. This detailed pseudocode outlines the MATLAB-specific steps involved in simulating and analyzing vehicle performance for different propulsion technologies. It covers the development of simulation models, performance analysis, parameter optimization, and interpretation of results. Each step is designed to provide a comprehensive understanding of the performance characteristics and interactions of various vehicle propulsion systems.

15. Acceleration Dfactor Working : The code first defines an array ne representing the engine speed in RPM over a range from 800 to 5600 with a step size of 10. Engine torque Te is calculated based on a polynomial formula involving engine speed and specific coefficients. Constants and variables representing various vehicle parameters are defined. Tractive force Ft is calculated for each gear using engine torque, gear ratios, efficiency, and tire radius. Vehicle velocity Vx is calculated for each gear using engine speed, gear ratios, and other constants. A rotation mass conversion factor dt is calculated for each gear, which relates to the dynamic power factor calculation. Aerodynamic drag resistance Ra is calculated for each gear based on speed, drag coefficient, and other constants. Dynamic power factors Dt are calculated for each gear by considering the difference between tractive force and drag resistance, normalized by mass and gravity. Vehicle acceleration a is calculated for each gear by accounting for gravity and rolling resistance. The final plot shows the relationship between vehicle speed (x-axis) and acceleration (y-axis) for each gear. Each gear is represented by a curve on the chart. As the speed of the vehicle increases, the acceleration curve shows how the vehicle's rate of change in speed changes with respect to speed itself. The x-axis represents vehicle speed in kilometers per hour (km/h). The y-axis represents acceleration in meters per second squared (m/s²).

16. Acceleration Dfactor

17. Acceleration Dfactor The acceleration factor, often denoted as "acceleration de factor," in the provided code is calculated as follows: For each gear, the acceleration factor (a1, a2, a3, a4, a5) is calculated using the following formula: a = g * (Dt - f) / dt Here's the breakdown of the formula components: a: This represents the acceleration factor for each gear (a1 for the first gear, a2 for the second gear, and so on). g: This is the acceleration due to gravity. Dt: Dynamic factor at each gear, calculated as (Ft - Ra) / (m * g), where Ft is the tractive force and Ra is the aerodynamic drag resistance for the respective gear. f: The coefficient of rolling resistance. dt: The rotation mass conversion factor specific to each gear. It is calculated as dt = 1.03 + 0.04 * ig , where ig is the gear ratio. The acceleration factor represents the acceleration in meters per second squared (m/s^2) that the vehicle can achieve in each gear at a given speed. It takes into account the available tractive force, rolling resistance, and aerodynamic drag.

18. Engine Fitting Before Engine Fitting

19. Engine Fitting After Engine Fitting

20. Engine fitting This code is related to engine torque data, where it appears to compare actual measured engine torque data with a fitted torque curve based on a mathematical model. The purpose of this code is to visually compare the measured engine torque data with a fitted engine torque curve based on a mathematical model. The code demonstrates how well the mathematical model represents the actual measured data. The first plot (figure (1)) shows the original measured torque data at specific RPM points, while the second plot (figure (2)) displays the fitted torque curve generated by the mathematical model across a wider range of RPM values. Such analysis is commonly used in engineering and automotive contexts to validate and refine models, assess their accuracy, and gain insights into the behavior of systems under study. In this case, the code could be part of an effort to understand the torque characteristics of an engine and how well a proposed mathematical model fits the observed data. Here are the formulas for the torque data before and after fitting: Before Fitting: n represents the engine speed in RPM (Revolutions Per Minute). T represents the engine torque at the respective engine speeds.

21. En Grade Angle and Vehicle Speed gine Fitting

22. En Grade Angle and Vehicle Speed gine Fitting The given form is structured to analyze and visually represent the impact of grade angles (slopes) on a vehicle's climbing capability at different speeds across various gears. Several factors, including engine torque, gear ratios, aerodynamic drag, rolling resistance, and mass, are taken into consideration. The purpose of this code is to illustrate how a vehicle's uphill performance is affected by different driving conditions. The graph depicted showcases the correlation between vehicle speed (x-axis) and the corresponding grade angle (y-axis) for distinct gears. Each gear is denoted by an individual curve on the graph, outlining the following key components: x-axis (Vehicle Speed): The horizontal axis symbolizes the vehicle's velocity in kilometers per hour (km/h), signifying its rate of motion. y-axis (Grade Angle): The vertical axis represents the grade angle measured in degrees. This angle signifies the slope of the road, indicating its steepness. Curves: The graph exhibits five distinct curves, each corresponding to a specific gear (1st, 2nd, 3rd, 4th, and 5th). These curves delineate the alteration in the vehicle's grade angle as its speed fluctuates within that gear. Labels: Adjacent to each curve's characteristic point, numerical labels are positioned to denote the corresponding gear number. Grid Lines: A grid system is in place to facilitate the accurate interpretation of values from the graph.

23. Grade Angle and Vehicle Speed

24. Grade Angle and Vehicle Speed The given form is structured to analyze and visually represent the impact of grade angles (slopes) on a vehicle's climbing capability at different speeds across various gears. Several factors, including engine torque, gear ratios, aerodynamic drag, rolling resistance, and mass, are taken into consideration. The purpose of this code is to illustrate how a vehicle's uphill performance is affected by different driving conditions. The graph depicted showcases the correlation between vehicle speed (x-axis) and the corresponding grade angle (y-axis) for distinct gears. Each gear is denoted by an individual curve on the graph, outlining the following key components: x-axis (Vehicle Speed): The horizontal axis symbolizes the vehicle's velocity in kilometers per hour (km/h), signifying its rate of motion. y-axis (Grade Angle): The vertical axis represents the grade angle measured in degrees. This angle signifies the slope of the road, indicating its steepness. Curves: The graph exhibits five distinct curves, each corresponding to a specific gear (1st, 2nd, 3rd, 4th, and 5th). These curves delineate the alteration in the vehicle's grade angle as its speed fluctuates within that gear. Labels: Adjacent to each curve's characteristic point, numerical labels are positioned to denote the corresponding gear number. Grid Lines: A grid system is in place to facilitate the accurate interpretation of values from the graph.

25. Tractive Effort

26. Tractive Effort The tractive effort and resistance in the provided code are calculated as follows: Tractive Effort (Ft) for each gear: Ft1, Ft2, Ft3, Ft4, Ft5 represent the tractive efforts for the first, second, third, fourth, and fifth gears, respectively. Ft = Te * ig * i0 * eta / r Te is the engine torque. ig is the gear ratio. i0 is a constant. eta is the transmission efficiency. r is the wheel radius. Rolling Resistance (Rr): Rr = m * g * f m is the mass of the vehicle. g is the acceleration due to gravity. f is the coefficient of rolling resistance.

27. Tractive Effort Aerodynamic Drag Resistance (Ra): Ra = CD * A * Vx^2 / 21.15 CD is the coefficient of aerodynamic drag. A is the vehicle's frontal area. Vx is the vehicle velocity in km/h. Total Resistance (Res): Res = Rr + Ra It is the sum of rolling resistance and aerodynamic drag resistance. The tractive effort represents the force generated by the engine to move the vehicle, while the resistance represents the forces opposing the vehicle's motion, including rolling resistance and aerodynamic drag. The balance between tractive effort and resistance determines the vehicle's speed.

28. Power Curve

29. Power curve The graph generated by the code displays important information about the vehicle's performance: Power vs. Velocity Curves for Gears: The graph includes five distinct curves, each representing the power needed to maintain different vehicle speeds across gears. The x-axis represents vehicle speed in kilometers per hour (km/h), while the y-axis indicates power in kilowatts (kW). The curves show how the power requirement changes with speed for each gear. As the speed increases, the power required generally increases as well. Maximum Available Power Line: A dashed red line indicates the maximum available power (Pe) for the vehicle. This line helps determine the upper limit of power available for propulsion. The intersection points of the gear power curves with this line reveal the feasible speed ranges for each gear, considering available power. Total Resistance Power Line: A dashed black line represents the total resistance power (P), which combines rolling resistance and aerodynamic drag resistance. The point where the total resistance power intersects with the maximum available power line determines the maximum vehicle speed achievable under the given resistive forces and power constraints. Maximum Speed Marker: A red asterisk (*) marks the specific point on the graph where the vehicle's maximum speed is attained. This point corresponds to the intersection of the total resistance power line and the maximum available power line. Annotations near the curves denote which gear each curve corresponds to (1st, 2nd, 3rd, 4th, 5th). Additionally, a label indicates the resistance power, and another highlights the maximum available power.

30. Power curve The vehicle power and maximum speed in the provided code are calculated as follows: Vehicle Power (Pe) for each gear: Pe1, Pe2, Pe3, Pe4, Pe5 represent the vehicle power for the first, second, third, fourth, and fifth gears, respectively. Pe = (Ft * Vx) / 3600 Ft is the tractive effort for each gear. Vx is the vehicle velocity in km/h. 3600 is used to convert from W (Watt) to kW (kilowatt). Rolling Resistance Power ( Pr ): Pr = (m * g * f * Vx) / 3600 m is the mass of the vehicle. g is the acceleration due to gravity. f is the coefficient of rolling resistance. Vx is the vehicle velocity in km/h. Aerodynamic Drag Resistance Power (Pa): Pa = (CD * A * Vx^3) / 76140 CD is the coefficient of aerodynamic drag. A is the vehicle's frontal area. Vx is the vehicle velocity in km/h. Total Resistance Power (P): P = Pr + Pa It is the sum of rolling resistance power and aerodynamic drag resistance power.

31. Power Performance Acceleration Time Acceleration Time and Peak Power

32. Power Performance Acceleration Time Acceleration Time and Vehicle Mass

33. Power Performance Acceleration Time Acceleration Time and Air Drag Coefficient

34. Power Performance Acceleration Time Graph 1: Acceleration Time vs. Peak Power A loop iterates over different peak power values ( Pm from 49 to 100). For each power value, it calculates the acceleration time for varying vehicle speeds. Tractive force, total resistance, acceleration, and acceleration time are calculated. The result is a graph of acceleration time against peak power. Graph 2: Acceleration Time vs. Vehicle Mass The peak power is set to 70 kW. A loop iterates over different vehicle mass values ( m from 1299 to 2000 kg). For each mass value, it calculates the acceleration time for varying vehicle speeds. Tractive force, total resistance, acceleration, and acceleration time are calculated. The result is a graph of acceleration time against vehicle mass. Graph 3: Acceleration Time vs. Air Drag Coefficient The vehicle mass is set to 1575 kg. A loop iterates over different air drag coefficient values ( Cd from 0.2 to 1 with a step of 0.01). For each drag coefficient value, it calculates the acceleration time for varying vehicle speeds. Tractive force, total resistance, acceleration, and acceleration time are calculated. The result is a graph of acceleration time against air drag coefficient.

35. Pure Electric Power Vehicle Tractive Force vs. Vehicle Speed

36. Pure Electric Power Vehicle This graph compares the tractive forces and resistance for the first and second gears of the electric vehicle. Tractive force is the force that propels the vehicle forward, and resistance includes both rolling resistance and aerodynamic drag. Red Line (First Gear Tractive Force): This line represents the tractive force in the first gear as a function of vehicle speed. It is calculated based on the motor's torque and the gear ratio of the first gear. The tractive force initially increases with vehicle speed, indicating the force required to overcome rolling resistance and aerodynamic drag. Beyond a certain speed, the tractive force decreases, as the motor's torque capability decreases at higher speeds. Blue Dashed Line (Second Gear Tractive Force): This line represents the tractive force in the second gear. Similarly to the first gear, it shows how the tractive force changes with vehicle speed. The dashed part of the line indicates that the force requirement exceeds the available force, and the vehicle cannot maintain acceleration beyond this point. Black Dotted Line (Total Resistance in Second Gear): This line represents the total resistance faced by the vehicle in the second gear, including both rolling resistance and aerodynamic drag. It starts at a higher value than the tractive force lines and decreases with increasing speed due to aerodynamic drag becoming dominant. Vertical Dashed Line (Max Speed in Second Gear): This vertical dashed line marks the point where the tractive force and total resistance intersect, indicating the vehicle's maximum speed in the second gear. The maximum speed occurs where the vehicle reaches equilibrium between available tractive force and resistance.

37. Pure Electric Power Vehicle Acceleration vs. Vehicle Speed

38. Pure Electric Power Vehicle Acceleration vs. Vehicle Speed (Graph 2): Compares the acceleration profiles in first and second gears. Demonstrates how the acceleration of the vehicle changes with vehicle speed for each gear. This graph compares the acceleration profiles for the first and second gears of the electric vehicle. Red Line (First Gear Acceleration): This line shows how the vehicle's acceleration changes with increasing vehicle speed in the first gear. The acceleration starts high and gradually decreases as the resistance forces become more prominent. Blue Dashed Line (Second Gear Acceleration): Similar to the first gear, this line represents the acceleration in the second gear. It shows how acceleration changes with vehicle speed in the second gear. The dashed part indicates that the vehicle's acceleration cannot be maintained beyond this point due to force limitations.   Climbing Ability vs. Vehicle Speed (Graph 3): Compares the maximum climbing abilities (grade) in first and second gears. Illustrates how the vehicle's ability to climb slopes changes with vehicle speed for each gear.

39. Parallel Hybrid Electric Vehicle NEDC Cycles

40. Parallel Hybrid Electric Vehicle

41. Parallel Hybrid Electric Vehicle

42. New European Driving Cycle (NEDC). NEDC Cycles

43. New European Driving Cycle (NEDC). The code is simulating and visualizing the vehicle speed profile during different driving cycles, specifically focusing on the urban and suburban driving portions of the New European Driving Cycle (NEDC). The purpose is to represent how the vehicle's speed changes over time during different driving scenarios, providing insights into acceleration, deceleration, and constant speed phases. axis([0 1200 0 120]) : This sets the range of the x-axis and y-axis for the graph that will be plotted later. The x-axis represents time (in seconds), and the y-axis represents vehicle speed (in km/h). Urban Driving Portion of the NEDC - First Urban Cycle Loop : This section represents the first loop of the urban driving portion of the NEDC. It simulates the vehicle speed profile during acceleration, constant speed, and deceleration phases for this loop. plot( t,u ) : This plots the vehicle speed profile over time for the first urban cycle loop. The time values are stored in the array t , and the corresponding vehicle speed values are stored in the array u . Extending Values for Other Urban Cycle Loops : The same speed profile is extended to subsequent urban driving cycle loops, effectively creating a continuous speed profile for the entire urban driving portion of the NEDC. Suburban Driving Portion of the NEDC : Similar to the urban portion, this section simulates the vehicle speed profile during the suburban driving portion of the NEDC. It includes acceleration, constant speed, and deceleration phases with appropriate speed values. xlabel ('Time(sec) ') ylabel ('Vehicle Speed /(km/h)') : These commands label the x-axis as "Time (sec)" and the y-axis as "Vehicle Speed (km/h)". grid on : This turns on the grid lines in the graph.

44. Conclusion and future work In summary, this research has conducted a thorough examination of diverse vehicle propulsion technologies in the automotive sector, including petrol, hybrid, and electric powertrains. The use of MATLAB has facilitated a detailed understanding of vehicle performance, contributing to the improvement of these technologies. The study successfully achieved its primary objectives, exploring torque characteristics, assessing road resistance impact, analyzing performance metrics, studying braking dynamics (particularly ABS), and simulating various driving range scenarios. The research initiated by generating characteristic curves to reveal the interaction between engine/motor torque and road resistance, utilizing MATLAB to model road resistance for a deeper comprehension of power performance. Evaluation of critical performance parameters, such as maximum vehicle speed and electric vehicle range, provided valuable insights for vehicle design and operation strategies. The analysis of braking performance, with a focus on ABS, not only emphasized stability enhancement but also offered essential metrics for assessing braking efficiency. Advancing to predict vehicle performance under specific driving conditions, dynamic characteristic graphs were employed to understand power factors influencing electric vehicle power performance. Particularly relevant to electric vehicles, this approach aided in simulating driving ranges at constant speeds, providing practical real-world application information. The study extended to BEVs and E-REVs, utilizing MATLAB simulations to align power characteristics with transmission systems and select motors that meet design requirements. Battery parameter matching ensured electric vehicle specifications for optimal performance. For FCEVs, a holistic approach involving mathematical modeling and parameter matching established an efficient power system. Additionally, driving range simulations under NEDC offered insights into cycle state management for electric vehicles, enhancing the practicality of the study's outcomes. Overall, this research contributes valuable insights to the understanding and improvement of vehicle propulsion technologies.

45. Conclusion and future work Future Work: Enhanced Energy Management: Explore advanced energy management strategies using sophisticated control algorithms, possibly incorporating machine learning or artificial intelligence, to dynamically optimize energy distribution based on real-time driving conditions, battery health, and energy availability. Real-world Validation: Conduct empirical field tests and gather more real-world data to validate the theoretical analyses and simulations, focusing on the efficiency, reliability, and long-term performance of diverse propulsion technologies under various conditions. Optimization of Storage Technologies: Further investigate the optimal combinations of energy storage technologies for different vehicle types, routes, and driving patterns. Conduct in-depth comparative studies considering efficiency, cost, weight, and lifespan trade-offs. Extended Range Strategies: Research optimal strategies for extending vehicle range, considering factors such as charging infrastructure availability, user convenience, and economic viability. Address challenges related to charging station placement, fast-charging technology, and grid impact for a fleet of hybrid and electric vehicles. Advanced Eco-Driving Algorithms: Develop more sophisticated and context-aware eco-driving algorithms that leverage vehicle characteristics, energy sources, and real-time traffic information for fuel cell vehicles and other hybrid systems. Solar Integration Optimization: Further investigate optimal strategies for integrating solar energy into vehicles, focusing on placement of solar panels and considering varying solar irradiance and weather conditions. Conduct comparative studies across different vehicle types. Grid Integration Research: Explore the integration of renewable energy sources with the grid, especially for remote areas or regions with limited energy infrastructure. Investigate technical, economic, and regulatory challenges for implementing renewable hybrid systems for both vehicle charging and grid support. Comprehensive Life Cycle Analysis: Conduct comprehensive life cycle analyses considering the environmental impact across the entire lifespan of hybrid and renewable energy systems. Assess production, usage, and end-of-life stages of energy storage components. Policy and Market Frameworks: Research the necessary policy, regulatory, and market frameworks to support widespread adoption of hybrid and renewable energy systems in vehicles. Understand the implications of government incentives, emissions regulations, and industry standards. Consumer Acceptance Studies: Investigate consumer behavior and preferences toward hybrid and renewable energy vehicles. Explore factors influencing consumer acceptance, willingness to adopt new technologies, and perceived benefits, providing insights for manufacturers, policymakers, and marketers.