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

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.

Figures (9)

• Figure 1: The input current $u_k$ and the estimated terminal voltage $\hat{y}_k$ of the JEKF on 36 hours of WLTP data (beginning-of-life), both without and with capacity updates.
• Figure 2: The estimated SOC $\hat{s}_k$ of the JEKF on 36 hours of WLTP data (beginning-of-life), both without and with capacity updates.
• Figure 3: The estimated capacity $\hat{C}_0$ and estimated JEKF parameters $\theta_1$, $\theta_2$, and $\theta_3$ on 36 hours of WLTP data (beginning-of-life), both without and with capacity updates, and once with variable $\theta_1$. Regions where the JEKF is marginally stable are shaded.
• Figure 4: The estimated capacity $\hat{C}_0$ based on 36 hours of WLTP data for three different aging states.
• Figure 5: The estimated SoC and JEKF parameters for three different aging states.