Battery state of health monitoring by estimation of the number of cyclable Li-ions

Zhou, Xin; Stein, Jeffrey L.; Ersal, Tulga

doi:10.1016/j.conengprac.2017.05.009

Battery state of health monitoring by estimation of the number of cyclable Li-ions

Journal Article · Wed Jun 28 00:00:00 EDT 2017 · Control Engineering Practice

DOI:https://doi.org/10.1016/j.conengprac.2017.05.009· OSTI ID:1533683

Zhou, Xin ^[1]; Stein, Jeffrey L. ^[2]; Ersal, Tulga ^[3]

Univ. of Michigan, Ann Arbor, MI (United States). Walter E. Lay Automotive Laboratory; DOE/OSTI
Univ. of Michigan, Ann Arbor, MI (United States). GG Brown Laboratories
Univ. of Michigan, Ann Arbor, MI (United States). Walter E. Lay Automotive Laboratory

Herein this paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise).

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE; USDOE Office of International Affairs (IA); USDOE Office of Policy (OP)

Grant/Contract Number:: PI0000012

OSTI ID:: 1533683

Alternate ID(s):: OSTI ID: 1702852

Journal Information:: Control Engineering Practice, Journal Name: Control Engineering Practice Journal Issue: C Vol. 66; ISSN 0967-0661

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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25 ENERGY STORAGE
Automation & Control Systems
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State of health

Battery state of health monitoring by estimation of the number of cyclable Li-ions

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