Prediction of charge-discharge behavior and state of charge estimation for tri-electrode rechargeable zinc-air flow batteries
6
Issued Date
2022-11-30
Resource Type
eISSN
2352152X
Scopus ID
2-s2.0-85139344482
Journal Title
Journal of Energy Storage
Volume
55
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Energy Storage Vol.55 (2022)
Suggested Citation
Lao-atiman W., Bumroongsri P., Arpornwichanop A., Olaru S., Kheawhom S. Prediction of charge-discharge behavior and state of charge estimation for tri-electrode rechargeable zinc-air flow batteries. Journal of Energy Storage Vol.55 (2022). doi:10.1016/j.est.2022.105786 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84543
Title
Prediction of charge-discharge behavior and state of charge estimation for tri-electrode rechargeable zinc-air flow batteries
Author's Affiliation
Other Contributor(s)
Abstract
An effective state of charge (SOC) estimation is essential for the development of a battery management system. At present, the provision of a SOC estimation for zinc-air flow batteries (ZAFBs) is still at its early stage of development. This work sets out to develop the SOC estimator for ZAFBs. The estimator is based-on a linear parameter varying (LPV) model integrated with an extended Kalman filter (EKF). The LPV model is constructed from multiple linear time-invariant (LTI) models with battery current and SOC as scheduling parameters. It is observed that the response data for ZAFBs have an exceptionally flat profile related to SOC change; dynamic differentiation only occurs when SOC is almost depleted. For this reason, the estimation of SOC converges to the true value when SOC is near depletion. In this work, it is shown that by appropriate tuning, the SOC estimation performance of the LPV model combined with EKF performs well as the absolute errors of soc. estimation lie under 2 % after true SOC convergence. The LPV model with the EKF algorithm is also compared with the Luenberger observer (LO). The proposed estimator can surpass the LO estimator. Overall, this SOC estimator provides a systematic way to fulfill the requirements of a battery management system.