An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles
This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly prop...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10019280/ |
| _version_ | 1828049115336933376 |
|---|---|
| author | Ali Wadi Mamoun Abdel-Hafez Hashim A. Hashim Ala A. Hussein |
| author_facet | Ali Wadi Mamoun Abdel-Hafez Hashim A. Hashim Ala A. Hussein |
| author_sort | Ali Wadi |
| collection | DOAJ |
| description | This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell’s dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented. |
| first_indexed | 2024-04-10T19:07:14Z |
| format | Article |
| id | doaj.art-e35b590767ca4204b40e38690f1c7d1d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T19:07:14Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e35b590767ca4204b40e38690f1c7d1d2023-01-31T00:00:36ZengIEEEIEEE Access2169-35362023-01-01118663867310.1109/ACCESS.2023.323797210019280An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive CyclesAli Wadi0https://orcid.org/0000-0002-4617-7026Mamoun Abdel-Hafez1https://orcid.org/0000-0002-9010-4094Hashim A. Hashim2https://orcid.org/0000-0003-2376-0603Ala A. Hussein3https://orcid.org/0000-0002-8867-3132Department of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Mechanical and Aerospace Engineering, Carleton University, Ottawa, CanadaFlorida Solar Energy Center, University of Central Florida, Orlando, FL, USAThis paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell’s dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented.https://ieeexplore.ieee.org/document/10019280/Extended Kalman filterinvariant extended Kalman filterEKFIEKF |
| spellingShingle | Ali Wadi Mamoun Abdel-Hafez Hashim A. Hashim Ala A. Hussein An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles IEEE Access Extended Kalman filter invariant extended Kalman filter EKF IEKF |
| title | An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles |
| title_full | An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles |
| title_fullStr | An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles |
| title_full_unstemmed | An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles |
| title_short | An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles |
| title_sort | invariant method for electric vehicle battery state of charge estimation under dynamic drive cycles |
| topic | Extended Kalman filter invariant extended Kalman filter EKF IEKF |
| url | https://ieeexplore.ieee.org/document/10019280/ |
| work_keys_str_mv | AT aliwadi aninvariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT mamounabdelhafez aninvariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT hashimahashim aninvariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT alaahussein aninvariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT aliwadi invariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT mamounabdelhafez invariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT hashimahashim invariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles AT alaahussein invariantmethodforelectricvehiclebatterystateofchargeestimationunderdynamicdrivecycles |