A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

Accurate state of charge (SOC) estimation is a fundamental guarantee for effective development of lithium-ion power battery in electric vehicles. To improve the SOC estimation precision and robustness, a novel model-based estimation approach has been proposed. Fully giving consideration to the effec...

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Main Authors: Zheng Liu, Xuanju Dang, Benqin Jing
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8689033/
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spelling doaj-a868287388f741589fd05c607b0812ca2021-03-29T22:17:59ZengIEEEIEEE Access2169-35362019-01-017494324944710.1109/ACCESS.2019.29108828689033A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman FilterZheng Liu0https://orcid.org/0000-0002-5733-0436Xuanju Dang1Benqin Jing2School of Electronic and Automation, Guilin University of Electronic Technology, Guilin, ChinaSchool of Electronic and Automation, Guilin University of Electronic Technology, Guilin, ChinaSchool of Electronic and Automation, Guilin University of Electronic Technology, Guilin, ChinaAccurate state of charge (SOC) estimation is a fundamental guarantee for effective development of lithium-ion power battery in electric vehicles. To improve the SOC estimation precision and robustness, a novel model-based estimation approach has been proposed. Fully giving consideration to the effect of measurement errors, the dynamic external electrical property of lithium-ion battery is approximated by a controlled auto-regressive and moving average (controlled ARMA)-based equivalent circuit model. An improved adaptive extended Kalman filter approach is developed for SOC estimation based on the multi-innovation principle. Meanwhile, the different weighting factor is added into each innovation to reduce cumulative influence of historical interference. Since the flat characteristic in OCV-SOC fitting curve enlarges the OCV-based SOC estimation error, a feedforward compensation method is introduced to reduce OCV identification error to improve OCV-based SOC estimation. The simulation and experimental results verify the validity of the proposed methodology over other estimation methods. Besides, simulated current noise is added to the condition data to prove the high precision and strong robustness of the proposed algorithm.https://ieeexplore.ieee.org/document/8689033/Lithium-ion batterystate of chargeKalman filtermulti-innovationOCV compensation
collection DOAJ
language English
format Article
sources DOAJ
author Zheng Liu
Xuanju Dang
Benqin Jing
spellingShingle Zheng Liu
Xuanju Dang
Benqin Jing
A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
IEEE Access
Lithium-ion battery
state of charge
Kalman filter
multi-innovation
OCV compensation
author_facet Zheng Liu
Xuanju Dang
Benqin Jing
author_sort Zheng Liu
title A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
title_short A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
title_full A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
title_fullStr A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
title_full_unstemmed A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
title_sort novel open circuit voltage based state of charge estimation for lithium-ion battery by multi-innovation kalman filter
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Accurate state of charge (SOC) estimation is a fundamental guarantee for effective development of lithium-ion power battery in electric vehicles. To improve the SOC estimation precision and robustness, a novel model-based estimation approach has been proposed. Fully giving consideration to the effect of measurement errors, the dynamic external electrical property of lithium-ion battery is approximated by a controlled auto-regressive and moving average (controlled ARMA)-based equivalent circuit model. An improved adaptive extended Kalman filter approach is developed for SOC estimation based on the multi-innovation principle. Meanwhile, the different weighting factor is added into each innovation to reduce cumulative influence of historical interference. Since the flat characteristic in OCV-SOC fitting curve enlarges the OCV-based SOC estimation error, a feedforward compensation method is introduced to reduce OCV identification error to improve OCV-based SOC estimation. The simulation and experimental results verify the validity of the proposed methodology over other estimation methods. Besides, simulated current noise is added to the condition data to prove the high precision and strong robustness of the proposed algorithm.
topic Lithium-ion battery
state of charge
Kalman filter
multi-innovation
OCV compensation
url https://ieeexplore.ieee.org/document/8689033/
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