In recent years, numerous control algorithms for connected and automated vehicles have emerged which focus on modifying driving strategy in order to reduce fuel usage. Referred to as “dynamic eco-driving,” these technologies have realized the possibility for additional fuel savings by utilizing information technologies rather than mechanics. The exact methodologies, however, are diverse. Three primary categories of dynamic eco-driving methodologies are identified and described: 1) ad-hoc methods, designed for the purpose of saving fuel but not considering optimality, 2) classical optimization methods, which use fuel usage modeling to solve an optimal control problem forwards in time, whether numerically or analytically, and 3) optimization by dynamic programming, in which a fuel usage-oriented cost function is minimized but solved backwards in time in discrete steps. Representatives from each of these categories are studied and implemented in simulation for comparison. Advantages and disadvantages of each relative to multiple performance measures are discussed.

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