Modelling (Aug 2024)

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

  • Lorenzo Breglio,
  • Arcangelo Fiordellisi,
  • Giovanni Gasperini,
  • Giulio Iodice,
  • Denise Palermo,
  • Manuela Tufo,
  • Fabio Ursumando,
  • Agostino Mele

DOI
https://doi.org/10.3390/modelling5030048
Journal volume & issue
Vol. 5, no. 3
pp. 911 – 935

Abstract

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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.

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