Immersion and Invariance Adaptive Observers for Online Electric Vehicle Mass Estimation

Accurate gross mass estimation is critical in operations of electric vehicles that experience payload variations due to its predominant impact on energy consumption and dynamics. This paper introduces a novel Immersion and Invariance (I&I) adaptive observer for estimating gross vehicle mass using only vehicle velocity and driving torque signals. Compared to conventional adaptive observer designs, our approach ofers two distinct advantages. First, the I&I scheme is “no regret”, ensuring that the norm of parameter estimation errors remains non-expansive. Second, the design enables asymptotic recovery of deterministic observer error dynamics, as the I&I adaptation drives parameter-error-induced perturbations toward an attractive and zeroing manifold. Additionally, an observer incorporating drivetrain inertia is proposed to account for rotational dynamics and enhance model fidelity. The effectiveness of the observers is demonstrated through high-fidelity, hardware-in-the-loop, experiments.