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Gear Shift Pattern Optimization for Best Fuel Economy, Performance and Emissions
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
As the FTP-75 drive cycle does not have a prescribed gear shift pattern, automotive OEMs have the flexibility to design. Conventionally, gear shift pattern was formulated based on trial and error method, typically with 10 to 12 iterations on chassis dynamometer. It was a time consuming (i.e. ~ 3 to 4 months) and expensive process. This approach led to declaring poor fuel economy (FE). A simulation procedure was required to generate a gear shift pattern that gives optimal trade-off amongst conflicting objectives (FE, performance and emissions). As a result, a simulation tool was developed in MATLAB to generate an optimum gear shift pattern. Three different SUV/UV models were used as test vehicles in this study. Chassis dyno testing was conducted, and data was collected using the base and optimized gear shift patterns. Dyno test results with optimized gear shift pattern showed FE improvement of ~ 4 to 5% while retaining the NOx margin well above engineering targets. This labeling FE improvement method did not require any hardware or software changes, thus, involved no additional expense. Above procedure was subsequently extended to new ICV (intermediate commercial vehicle) truck that required driveline ratio to be finalized at conceptual phase. FE simulations were conducted using RWUP (real world usage profile – vehicle velocity vs. time vs. road grade) cycle. Gear shift pattern was optimized for each plausible driveline ratio and the value with best combination of FE and performance was chosen. ICV truck built with this driveline configuration received excellent feedback on FE and drivability by the expert jury. This enabled ICV truck to be launched with a driveline ratio that met “first time right” quality crusade while conforming to strict project deadlines. The methodology developed can be used to devise gear shift indicator (GSI) algorithm as well.