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Research Papers

Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical–Thermal Model

[+] Author and Article Information
S. Dey

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

B. Ayalew

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

P. Pisu

Department of Automotive Engineering,
Clemson University,
4 Research Drive,
Greenville, SC 29607
e-mail: pisup@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 December 17, 2014; final manuscript received June 18, 2015; published online August 13, 2015. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 137(11), 111005 (Aug 13, 2015) (12 pages) Paper No: DS-14-1535; doi: 10.1115/1.4030972 History: Received December 17, 2014

Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunov's stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.

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Figures

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Fig. 1

Illustration of SPM with discretized nodes

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Fig. 3

Adaptive observer scheme

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Fig. 4

Temperature and voltage estimation performance

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Fig. 5

Bulk SOC and surface concentration estimation performance

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Fig. 6

Parameter estimation performance

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Fig. 2

Output voltage (y1) as a function of surface concentration (x1M)

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Fig. 7

Voltage and temperature estimation performance for 2C constant discharge profile

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Fig. 8

Surface and bulk SOC estimation performance for 2C constant discharge profile

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Fig. 9

Parameter estimation performance for 2C constant discharge profile

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Fig. 10

Applied current, voltage, and temperature estimation performance for pulse discharge profile

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Fig. 11

Surface and bulk SOC Estimation performance for pulse discharge profile

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Fig. 12

Parameter estimation performance for pulse discharge profile

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Fig. 13

Performance of the adaptive observer scheme in presence of output uncertainties and absence of input current

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Fig. 14

Percentage errors in state and parameter estimation with high initial error in B˜M

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