LAUSR

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.