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Dynamic vehicle loads play critical roles for automotive controls including battery management, transmission shift scheduling, distance-to-empty predictions, and various active safety systems. Accurate real-time estimation of vehicle loads such as those due to vehicle mass and road grade can thus improve safety, efficiency, and performance. While several estimation methods have been proposed in literature, none have seen widespread adoption in current vehicle technologies despite their potential to significantly improve automotive controls. To understand and bridge the gap between research development and wider adoption of real-time load estimation, this paper assesses the accuracy and performance of four estimation methods that predict vehicle mass and/or road grade.

The design discipline of Design For Six Sigma (DFSS) has been applied to many areas of product development and manufacturing. As DFSS application has recently been extended to upfront automotive engineering areas such as research and advanced development, more robust and optimized technologies can be achieved in the pre-production stage, reducing cost, exhibiting superior quality and performance, and shortened development cycle. This paper describes the application of the DFSS process, Define, Characterize, Optimize, and Verify (DCOV) to develop an automotive technology that begins from the conceptual phase and continues up through the implementation phase. The role of DFSS in the automotive industry is to provide a framework for more rigorous upfront engineering. It provides guidelines to a more effective and efficient development of new technologies.