HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort

TL;DR

This paper introduces a real-time, online method for estimating battery capacity and internal resistance in electric vehicles using a joint Kalman filter approach, requiring minimal prior cell characterization.

Contribution

The paper presents a computationally efficient online estimation method combining a Joint Extended Kalman Filter with Recursive Least Squares, reducing the need for detailed prior cell models.

Findings

Algorithm converges on data from aged cells

Capacity and resistance follow expected aging trends

Demonstrated real-time updates in a Hardware-in-the-Loop setup

Abstract

Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.