A virtual sensor fusion approach for state of charge estimation of lithium-ion cells

TL;DR

This paper introduces a novel virtual sensor fusion method combining data-driven models, observers, and machine learning to improve the accuracy and smoothness of lithium-ion cell state of charge estimation.

Contribution

It presents a new approach that integrates virtual sensors, affine parameter-varying models, and machine learning with Kalman filtering for enhanced SOC estimation.

Findings

Improved SOC estimation accuracy

Enhanced smoothness of predictions

Effective data-driven calibration strategy

Abstract

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.