Abstract
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.
Highlights
Modern battery management systems (BMSs), among other functionalities, include a number of algorithms for estimating key battery state variables such as state-of-charge (SoC) and remaining available charge capacity, and model parameters such as internal resistance [1]
Since the battery SoC cannot be measured, and there is no fully reliable SoC estimate available, the dual extended Kalman filter-based (DEKF) accuracy is evaluated by analyzing a posteriori voltage residual, i.e., difference between the recorded voltage Ub and the voltage calculated from output
Algorithm for dualis estimation state-of-charge (SoC)battery and remaining capacity has been which aimed toofbebattery accurate over the whole lifetime charge and real-driving has been including proposed, varying which isambient aimed to be accurate over battery lifetime and real-driving conditions temperatures
Summary
Modern battery management systems (BMSs), among other functionalities, include a number of algorithms for estimating key battery state variables such as state-of-charge (SoC) and remaining available charge capacity, and model parameters such as internal resistance [1]. The internal resistance and capacity estimates are the main indicators used for tracking the battery degradation level, i.e., estimation of battery state-of-health (SoH) [2]. Almost every battery model parameter is changing with battery degradation, so that for robust SoC and SoH estimation, those changes should be accurately tracked, as well. Battery state and parameter estimation algorithms are often based on Kalman filters (KF), which in its basic linear version represent an optimal recursive solution for estimating hidden states of a linear, time-varying Gaussian system (i.e., probabilistic inference) [3]. Two of the most widely used nonlinear KFs are extended
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