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A DFSS Approach to Determine Automatic Transmission Gearing Content for Powertrain-Vehicle System Integration

机译:DFSS方法来确定动力总成车辆系统集成的自动变速器传动速度

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This investigation utilizes a DFSS analysis approach to determine automatic transmission gear content required to minimize fuel consumption for various powertrain - vehicle systems. L18 and L27 inner arrays with automatic transmission design and shift pattern constraint parameters were varied to determine their relative influence on fuel consumption. An outer noise array consisting of two vehicles with various engines, final drive ratios and legislated emissions test cycles was used to make a robust transmission selection based on minimizing fuel consumption. The full details of the DFSS analysis method and assumptions are presented along with a detailed examination of the results. With respect to transmission design parameters, parasitic spinloss and gear mesh efficiency were found to be most important followed by the number of gears. The DFSS analysis further revealed that unique transmission design formulations are potentially required for widely varying engines. The shift pattern constraint of minimum operating speed in gear that establishes the downshift line was found to be most significant while all other shift parameters proved to be negligible. An average 0.4 to 0.8% decrease in fuel consumption could be realized by optimizing the transmission for a given engine - vehicle combination with the underlying assumptions of the energy analysis. For a given transmission - vehicle, changing the engine resulted in an average 2 to 5% reduction in fuel consumption depending on the specifics of the engine technology.
机译:该研究利用DFSS分析方法来确定最小化各种动力总成车辆系统的燃料消耗所需的自动变速器齿轮含量。 L18和L27内部阵列具有自动变速器设计和移位模式约束参数,以确定它们对燃料消耗的相对影响。用于基于最小化燃料消耗的燃料消耗,使用由具有各种发动机,最终驱动比和立法排放测试循环的两个车辆组成的外部噪声阵列。 DFSS分析方法和假设的完整细节以及对结果的详细检查。关于传输设计参数,发现寄生型旋转螺旋和齿轮网效率最重要,然后是齿轮的数量。 DFSS分析进一步揭示了广泛变化的发动机可能需要独特的传动设计配方。发现建立降档线的齿轮最小操作速度的变速模式约束是最显着的,而所有其他换档参数被证明可以忽略不计。通过优化给定发动机的传输与能量分析的潜在假设来实现燃料消耗的平均降低0.4至0.8%。对于给定的传输 - 车辆,改变发动机导致燃料消耗的平均减少平均2%至5%,这取决于发动机技术的具体细节。

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