An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model
Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to...
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| Published in | Energy (Oxford) Vol. 115; pp. 219 - 229 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.11.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy.
•A novel space state equation is built to describe the pack dynamic behavior.•The dual filters method is used to estimate the pack state-of-charge.•Battery inconsistency is considered to analyze the pack usage efficiency.•The accuracy of the proposed method is verified under different conditions. |
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| AbstractList | Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy.
•A novel space state equation is built to describe the pack dynamic behavior.•The dual filters method is used to estimate the pack state-of-charge.•Battery inconsistency is considered to analyze the pack usage efficiency.•The accuracy of the proposed method is verified under different conditions. Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy. |
| Author | Chen, Zonghai Yang, Duo Wang, Yujie Zhang, Xu |
| Author_xml | – sequence: 1 givenname: Xu orcidid: 0000-0003-3348-4200 surname: Zhang fullname: Zhang, Xu – sequence: 2 givenname: Yujie surname: Wang fullname: Wang, Yujie – sequence: 3 givenname: Duo surname: Yang fullname: Yang, Duo – sequence: 4 givenname: Zonghai orcidid: 0000-0001-9312-9089 surname: Chen fullname: Chen, Zonghai email: chenzh@ustc.edu.cn |
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| Keywords | State-of-charge Battery inconsistency Extend Kalman filter-unscented Kalman filter Battery pack model |
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| SubjectTerms | algorithms Battery inconsistency Battery pack model electric vehicles energy efficiency Extend Kalman filter-unscented Kalman filter filters lithium batteries State-of-charge |
| Title | An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model |
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