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

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....

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Veröffentlicht in:Energies (Basel) 2020-05, Vol.13 (9), p.2138
Hauptverfasser: Park, Jinhyeong, Lee, Munsu, Kim, Gunwoo, Park, Seongyun, Kim, Jonghoon
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container_issue 9
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container_title Energies (Basel)
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creator Park, Jinhyeong
Lee, Munsu
Kim, Gunwoo
Park, Seongyun
Kim, Jonghoon
description 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.
doi_str_mv 10.3390/en13092138
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source DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute
subjects Aging
Algorithms
Alternative energy sources
Autoregressive models
Autoregressive processes
battery degradation
dual extended Kalman filter
Energy storage
Extended Kalman filter
Historical account
Integrated approach
Li-ion battery
Machine learning
Mathematical models
Methods
Multivariate analysis
Parameter identification
Prediction models
State of charge
Statistical methods
statistical model
Statistical models
title Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH
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