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Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method
Author(s) -
Gong Dongliang,
Gao Ying,
Kou Yalin
Publication year - 2021
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202100235
Subject(s) - state of charge , residual , voltage , robustness (evolution) , open circuit voltage , battery (electricity) , extended kalman filter , lithium ion battery , kalman filter , control theory (sociology) , equivalent circuit , engineering , computer science , algorithm , chemistry , electrical engineering , power (physics) , biochemistry , physics , control (management) , quantum mechanics , artificial intelligence , gene
Herein, the improved open‐circuit voltage (OCV) estimation method is developed and applied to estimate the model parameters and state of charge (SOC) for lithium‐ion batteries simultaneously. First, the OCV and SOC mapping relationship with temperature dependence is explored with the help of the low‐current OCV test and the improved OCV estimation method is proposed for all test temperatures. Afterward, the dual adaptive extended Kalman filter (DAEKF) based on the residual sequence is utilized to identify the model parameters and estimate the SOC simultaneously. Finally, the proposed approach is verified with 50% initial SOC error and compared with the residual sequence‐based DEKF method at different temperatures under the Federal Urban Driving Schedule (FUDS) test. The results of this study indicate that the proposed DAEKF based on the proposed improving OCV estimation method and residual sequence could achieve higher SOC estimation accuracy with good robustness at all test temperatures.