Sistema de gerenciamento de baterias de lítio com estimação de estados com filtro de Kalman estendido adaptativo.

Abstract

DA SILVA, C. T. Lithium battery management system with adaptive extended Kalman filter state estimation / C. T. Silva. ver. corr. São Paulo, 2022. 146p.Perform energy storage efficiently is one of the main factors for a sustainable world.Embedded electronics in energy storage systems plays an extremely important role in ensuring the systems efficiency, safety, and performance. This thesis presents the methodology for developing a complete battery management system (BMS), capable of properly protect and manage any battery (since the voltage, current and temperature limits are known), in any application (since the battery dynamic use are known). The battery mathematical model was developed with a focus on practical applications, comparing four different models, four optimization algorithms and seven experiments, in order to develop the best mathematical model with the best optimization method and the best experiment for LiFePO4 batteries used in electric forklifts. The mathematical model developed has unique and innovative features, which uses a multiples output structure, being able to improve the identified parameters accuracy by up to 100 times when compared to traditional models that use only the battery voltage as the system output. The presented methodology allows to create the battery management system algorithms, mainly for the state of charge estimation, which is done through and Adaptive Extended Kalman filter. The work innovates by also creating a policy to adjust the Kalman filter process noise (Q) and measurement noise (R) matrices at runtime. The filter algorithm, together with the mathematical model, achieved an average accuracy of 99,56% in real tests, in relation to the estimated and measured battery voltage. A cell balancing strategy was also implemented, capable of guaranteeing safety and efficiency of the battery pack in all tests performed. This work presents all the methods, equations, and simulations necessary for the battery management system development and applies in a real environment. The BMS hardware and firmware were developed, tested, and validated on a LiFePO4 8 cells battery pack, achieving excellent performance in all tests performed.