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State of charge estimation of a Li‐ion battery based on extended Kalman filtering and sensor bias
Author(s) -
AlGabalawy Mostafa,
Hosny Nesreen S.,
Dawson James A.,
Omar Ahmed I.
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6265
Subject(s) - extended kalman filter , state of charge , kalman filter , battery (electricity) , control theory (sociology) , voltage , invariant extended kalman filter , engineering , noise (video) , computer science , electrical engineering , power (physics) , artificial intelligence , physics , control (management) , quantum mechanics , image (mathematics)
Summary The growing usage of electric vehicles (EVs) has led to significant advancements in batteries' technology. State of charge (SOC) estimation is an essential function of the battery management system—the heart of EVs and Kalman filtering is a standard SOC estimation method. Because of the non‐uniformities in tuning and testing scenarios, it is challenging to quantify SOC estimation algorithms' performance. A SOC estimation algorithm is developed in this work, extended Kalman filter (EKF), and tested for variable scenarios like adding sensor noise and bias to terminal voltage and current and varying state and parameter initializations. Also, a dual EKF is implemented to estimate the sensor voltage and current bias and compared it against the state EKF to estimate SOC. Finally, a comparative study has been introduced to decide which algorithm represents the most accurate estimation for the battery parameters, and it was found that the dual EKF gave the best results.