A Model-Based Strategy for Active Balancing and SoC and SoH Estimations of an Automotive Battery Management System

Author:

Breglio Lorenzo1,Fiordellisi Arcangelo1ORCID,Gasperini Giovanni1,Iodice Giulio1,Palermo Denise1,Tufo Manuela12ORCID,Ursumando Fabio1,Mele Agostino13ORCID

Affiliation:

1. Kineton S.r.l.-Società Benefit, 80146 Napoli, Italy

2. Department of Engineering, University of Sannio, 82100 Benevento, Italy

3. Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa, Italy

Abstract

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation.

Publisher

MDPI AG

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