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In the present work, a computational routine was developed in order to optimize the shifting strategies implemented in a Gear Shift Indicator to assist the driver of manual transmission vehicles according to a desired driving style. In order to achieve this goal, a simulation algorithm was developed to estimate the fuel consumption and acceleration performance of a vehicle equipped with a 5-speed manual gearbox propelled by a 1.4 L engine when submitted to the FTP-72 driving cycle. A multi-objective Genetic Algorithm was then employed to solve the optimization problem for both fuel consumption and performance. Among the set of optimized solutions, 3 shifting strategies were selected for experimental evaluation on a chassis dynamometer. The experiments showed that the strategy with focus on fuel economy was able to save 10.7% of fuel, being the best performance strategy responsible for enhancing the vehicle acceleration in 8.33%, when compared to the standard strategy provided by the vehicle manufacturer. Finally, the best-compromised solution presented 6.4% fuel-savings with an acceleration performance similar to the best performance strategy.

mechanics based design of structures and machines

Experimental validation for the employment of shifting strategies optimized via i-AWGA in a gear shift indicator system for manual transmission vehicles