Work

Immersion and Invariance Adaptive Observers for Online Electric Vehicle Mass Estimation

Year: 2025

Type: article

Abstract: 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.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.007

Authors: Hyunjin Ahn, Xingyu Zhou, Zifei Su, Pingen Chen, Junmin Wang

Institutions: The University of Texas at Austin, Tennessee Technological University

Language: English

Topic: Vehicle Dynamics and Control Systems

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: U.S. Department of Energy