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Research Papers

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

[+] Author and Article Information
Satadru Dey

Department of Civil and Environmental Engineering,
University of California, Berkeley,
Davis Hall,
Berkeley, CA 94720
e-mail: satadru86@berkeley.edu

Beshah Ayalew

Department of Automotive Engineering,
Clemson University,
4 Research Drive,
Greenville, SC 29607
e-mail: beshah@clemson.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 10, 2015; final manuscript received September 8, 2016; published online January 16, 2017. Assoc. Editor: Ardalan Vahidi.

J. Dyn. Sys., Meas., Control 139(3), 031007 (Jan 16, 2017) (10 pages) Paper No: DS-15-1432; doi: 10.1115/1.4034801 History: Received September 10, 2015; Revised September 08, 2016

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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Figures

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Fig. 1

Schematic of the SPM

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Fig. 2

The functions UP, UN, UPD, and UND for LiCoO2–graphite chemistry [23]

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Fig. 4

Temperature and voltage tracking

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Fig. 5

Surface concentration tracking by observer II

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Fig. 6

Negative and positive electrode bulk SOC estimation performance

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Fig. 7

Pseudomeasurement and capacity estimation performance

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Fig. 8

Estimation performance of the observer-based scheme under dynamic discharge profile

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Fig. 9

Comparison of the estimation performance “with the conservation assumption” and the proposed scheme

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Fig. 10

Capacity fade tracking performance

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Fig. 11

Bulk SOC estimation performance under lithium plating (with C/10 charging and ambient temperature −20 °C). The plating is injected at t = 600 s.

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Fig. 12

Lithium plating detection performance with C/10 charging and ambient temperature −20 °C. The plating is injected at t = 600 s, and the Li-ion extraction rate from positive electrode (EP) goes beyond the maximum limit.

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Fig. 13

Bulk SOC estimation error under different levels of temperature measurement noise

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Fig. 14

Bulk SOC estimation error under parametric uncertainties (Rf,ref, h, and Cp)

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Fig. 15

Estimation error under zero current. The current is made to zero at 1000 s.

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