A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
This work addresses battery management for electric vehicles or energy storage systems, but it is incremental as it builds on existing paradigms.
The paper tackles the problem of estimating the State of Charge (SOC) in lithium-ion cells by combining Kalman Filters with machine-learning approaches, resulting in improved accuracy and smoothness in SOC estimation.
This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness.