Abstract
For complex energy storage conditions, it is necessary to monitor the state-of-charge (SOC) and closed-circuit voltage (CCV) status accurately for the reliable power supply application of lithium-ion batteries. Herein, an improved compound correction-electrical equivalent circuit modeling (CC-EECM) method is proposed by considering the influencing effects of ambient temperature and charge–discharge current rate variations to estimate the CCV. Then, an improved adaptive double transform-unscented Kalman filtering (ADT-UKF) method is constructed with recursive sampling data correction to estimate the nonlinear SOC. A dynamic window function filtering strategy is constructed to obtain the new sigma point set for the online weighting coefficient correction. For a temperature range of 5–45 °C, the CCV for the improved CC-EECM responds well with a maximum error of 0.008608 V, and the maximum SOC estimation error is 6.317%. The proposed ADT-UKF method improves the CCV and SOC estimation reliability and adaptability to the time-varying current rate, temperature, and aging factors.
Original language | English |
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Article number | 2200921 |
Journal | Energy Technology |
Volume | 10 |
Issue number | 12 |
ISSN | 2194-4288 |
DOIs | |
Publication status | Published - Dec 2022 |
Bibliographical note
Funding Information:The work is supported by the National Natural Science Foundation of China (No. 62173281).
Publisher Copyright:
© 2022 Wiley-VCH GmbH.
Keywords
- adaptive double transform-unscented Kalman filter
- closed-circuit voltage
- compound correction-electrical equivalent circuit modeling
- lithium-ion batteries
- state-of-charge