A Practical Methodology for Real-Time Adjustment of Kalman Filter Process Noise for Lithium Battery State-of-Charge Estimation
Source abstract
Abstract
The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process (Q) and measurement (R) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests.
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Research context
OpenAlex topics: Advanced Battery Technologies Research · Control Systems and Identification · Sensor Technology and Measurement Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
PEA—Polytechnic School (POLI-USP), University of São Paulo, São Paulo 05508-010, Brazil
INESC TEC and Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Funding
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 · Fundação de Desenvolvimento da Pesquisa (FUNDEP) Rota 2030/Linha V · National Funds through the Portuguese funding agency, FCT—Fundação para Ciência e a Tecnologia
References (27)
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