Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Jinhyeong Park, Munsu Lee, Gunwoo Kim, Seongyun Park and Jonghoon Kim
Additional contact information
Jinhyeong Park: Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea
Munsu Lee: Department of Energy Science, Sungkyunkwan University, Suwon 16419, Korea
Gunwoo Kim: Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea
Seongyun Park: Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea
Jonghoon Kim: Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea

Energies, 2020, vol. 13, issue 9, 1-20

Abstract: To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.

Keywords: Li-ion battery; battery degradation; statistical model; dual extended Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/9/2138/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/9/2138/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:9:p:2138-:d:351867

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().