VEHICLE GEAR SHIFTING CO-SIMULATION TO OPTIMIZE PERFORMANCE AND FUEL CONSUMPTION IN THE BRAZILIAN STANDARD URBAN DRIVING CYCLE

Eckert, Jony Javorski; Santiciolli, Fabio Mazzariol; Costa, Eduardo dos Santos; Corrêa, Fernanda Cristina; Dionísio, Heron José; Dedini, Franco Giuseppe · 2014 · OpenAlex-citations

DOI: 10.5151/engpro-simea2014-81

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Summary

This study addresses the optimization of vehicle gear-shifting strategies to balance performance and fuel consumption within the Brazilian Standard Urban Driving Cycle (NBR 6601). Motivated by Brazil’s INOVAR-AUTO regulation, which mandates significant reductions in vehicle fuel consumption, the research investigates how shifting tactics influence longitudinal dynamics. While performance-oriented strategies (maximizing torque or power) are well-defined, fuel-economy strategies are complex due to dependencies on vehicle speed, engine efficiency, and traction limits. The authors aim to develop an algorithm that selects the most adequate shifting tactic for each segment of the driving cycle to optimize both metrics simultaneously. The methodology employs a co-simulation platform integrating Adams™ for multibody dynamics and Matlab/Simulink™ for control logic and vehicle dynamics equations. The model represents a compact hatchback with a 1.0L engine, neglecting suspension effects to simplify the longitudinal analysis. The simulation accounts for aerodynamic drag, rolling resistance, climbing resistance, and powertrain inertia. Engine torque and specific fuel consumption are derived from experimental lookup tables based on engine speed and throttle angle. The study first evaluates five standard shifting strategies: fuel economy (shifting near 3000 rpm), intermediate shifts (3500 and 4500 rpm), maximum torque (5300 rpm), and maximum power (6400 rpm). Subsequently, two optimization algorithms are developed. These algorithms analyze a database of simulation results to select the optimal tactic for specific stretches of the NBR 6601 cycle, creating a dynamic control vector that adjusts shifting points based on real-time power demand and efficiency regions. Results from the standard strategies demonstrate a trade-off between performance and efficiency. Strategies maximizing power and torque achieved higher linear correlations with the standard velocity profile (0.9984 and 0.9983, respectively) and greater traveled distances but incurred significantly higher fuel consumption (7.05 km/L and 8.26 km/L). Conversely, the fuel economy strategy yielded lower performance correlation (0.9978) and shorter distance but better average consumption (11.22 km/L). The performance optimization algorithm achieved a correlation similar to the maximum torque strategy (0.9983) while improving fuel consumption by 32.69% compared to that baseline. The average fuel consumption optimization algorithm maintained the correlation of the standard fuel economy tactic (0.9978) but improved average fuel consumption by 18.45%. The optimized profiles revealed that non-uniform shifting, including skipping gears or delaying shifts during low-power requests, was superior to constant-speed shifting rules. The significance of this work lies in demonstrating that adaptive gear-shifting algorithms can simultaneously enhance vehicle performance and fuel efficiency, outperforming static strategies. The findings highlight that knowledge of engine efficiency maps and torque curves is crucial for defining dynamic models. By tailoring shifting tactics to specific driving cycle segments, manufacturers can meet regulatory fuel consumption targets without compromising vehicle acceleration performance. This approach provides a framework for developing intelligent transmission control systems that respond to real-time driving conditions rather than relying on fixed shifting points.

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• eco driving