A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model

Meng Jinhao, Daniel-Ioan Stroe, Mattia Ricco, Luo Guangzhao, Remus Teodorescu

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
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Abstract

The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM).
Original languageEnglish
Article number8536907
JournalI E E E Transactions on Industrial Electronics
Volume66
Issue number10
Pages (from-to)7717 - 7727
Number of pages11
ISSN0278-0046
DOIs
Publication statusPublished - Oct 2019

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Kalman filters
Identification (control systems)
Lithium-ion batteries
Extended Kalman filters
Equivalent circuits
Support vector machines
Capacitance
Experiments

Keywords

  • State-of-charge estimation
  • Partial least squares regression
  • Kalman filter
  • Lithium-ion battery

Cite this

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title = "A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model",
abstract = "The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM).",
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A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model. / Jinhao, Meng; Stroe, Daniel-Ioan; Ricco, Mattia; Guangzhao, Luo; Teodorescu, Remus.

In: I E E E Transactions on Industrial Electronics, Vol. 66, No. 10, 8536907, 10.2019, p. 7717 - 7727.

Research output: Contribution to journalJournal articleResearchpeer-review

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