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    Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

    Source: Journal of Dynamic Systems, Measurement, and Control:;2017:;volume( 139 ):;issue: 003::page 31007
    Author:
    Dey, Satadru
    ,
    Ayalew, Beshah
    DOI: 10.1115/1.4034801
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.
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      Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

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    contributor authorDey, Satadru
    contributor authorAyalew, Beshah
    date accessioned2017-11-25T07:20:39Z
    date available2017-11-25T07:20:39Z
    date copyright2017/16/1
    date issued2017
    identifier issn0022-0434
    identifier otherds_139_03_031007.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4236599
    description abstractThis paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleReal-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling
    typeJournal Paper
    journal volume139
    journal issue3
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4034801
    journal fristpage31007
    journal lastpage031007-10
    treeJournal of Dynamic Systems, Measurement, and Control:;2017:;volume( 139 ):;issue: 003
    contenttypeFulltext
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