  This paper investigates the theoretical Cramer-Rao bounds on estimation
accuracy of longitudinal vehicle dynamics parameters. This analysis is
motivated by the value of parameter estimation in various applications,
including chassis model validation and active safety. Relevant literature
addresses this demand through algorithms capable of estimating chassis
parameters for diverse conditions. While the implementation of such algorithms
has been studied, the question of fundamental limits on their accuracy remains
largely unexplored. We address this question by presenting two contributions.
First, this paper presents theoretical findings which reveal the prevailing
effects underpinning vehicle chassis parameter identifiability. We then
validate these findings with data from on-road experiments. Our results
demonstrate, among a variety of effects, the strong relevance of road grade
variability in determining parameter identifiability from a drive cycle. These
findings can motivate improved experimental designs in the future.
