Powertrain Model for Selection of Reduction Ratio and Estimation of Energy Requirement

Sakhalkar, Siddhesh V.; Dhillon, Parveen; Bakshi, Soovadeep; Kumar, Pranay; Arora, Puneet

doi:10.4271/2014-01-1781

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…However, the driving cycle for a performance vehicle on the race track is not widely available, and hence this poses a challenge for engineers trying to optimize formula racing vehicles. Additionally, most research studies are simply focused on the optimization of fuel consumption via the gear shifting strategy or gear ratio tuning without taking into account the performance of the vehicle [4,[8][9][10][11]. Furthermore, researchers paid more attention to passenger and commercial vehicle in which vehicle performance is not a critical aspect of the vehicle design.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Tey

Shak

2019

SN Appl. Sci.

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Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the ontrack performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study; especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

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Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Tey

Shak

2019

SN Appl. Sci.

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“…Sakhalkar et al avoid this by using a longitudinal vehicle model which includes longitudinal load transfer under acceleration [6]. The vehicle treats corners as an equivalent straight section with a velocity restriction and thus reduces the track to a longitudinal problem.…”

Section: Introductionmentioning

confidence: 99%

Lap Time Simulation Tool for the Development of an Electric Formula Student Car

Doyle¹,

Cunningham²,

White³

et al. 2019

SAE Technical Paper Series

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This work details the development of a lap time simulation (LTS) tool for use by Queen's University Belfast in the Formula Student UK competition. The tool provides an adaptable, user-friendly virtual test environment for the development of the team's first electric vehicle. A vehicle model was created within Simulink, and a series of events simulated to generate the performance envelope of the car in the form of maximum combined lateral/longitudinal accelerations against velocity (ggv diagram). A four-wheeled vehicle including load transfer was modelled, capturing shifts in traction between each tire, which can influence performance in vehicles where the total tractive power is split between individual wheel motors. The acceleration limits in the ggv diagram were used to simulate the acceleration and endurance events at Formula Student. These events were simulated using a MATLAB code considering a point mass, quasi-steady state model with a perfect driver. This method considering all four wheels captures performance characteristics that point mass models normally cannot, without the complexity and time required for more detailed LTS solutions including yaw movements and driver models. It also separates the vehicle model from the MATLAB code required to run the LTS, reducing the complexity of implementing future changes. The LTS was benchmarked against the freeware tool OptimumLap and validated where possible against competition results. A Latin Hypercube sampling technique was employed to generate numerous input scenarios for the simulation, and a response surface fitted to the results to perform a sensitivity analysis. Results from this analysis indicate several areas to efficiently focus future resource allocation as well as attempting to quantify trade-offs. Optimum powertrain gear ratio and battery capacity for a proposed vehicle were specified using the tool.

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Powertrain Model for Selection of Reduction Ratio and Estimation of Energy Requirement

Cited by 2 publications

References 5 publications

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Lap Time Simulation Tool for the Development of an Electric Formula Student Car

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