engleski

Introducing state variance coupling within a multi-timescale Kalman filter for improved Li-ion battery capacity estimation convergence properties

Estimators of lithium-ion battery states and parameters are usually divided in two coupled estimators realized in different timescales and based upon a battery equivalent circuit model (ECM). The estimator of battery state-of-charge (SoC) and ECM impedance parameters operates in the fast time scale, while the estimator of battery remaining charge capacity executes in the slow time scale. The paper presents an adaptive variance-coupling of SoC and capacity estimators aimed at improving the overall estimation performance in terms of accuracy and convergence speed. The emphasis is on presenting a detailed simulation analysis of the adaptively-coupled multi-timescale estimator features, including the convergence rate, parametrization robustness and capacity fade tracking.

Battery Management Systems (BMS) ; Estimation technique ; Batteries ; Automotive application

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