Electro-thermal modeling of Lithium-ion cell for higher discharge rate applications

Sangiri, Jeemut Bahan; Ghosh, Sudipto; Chakraborty, Chandan

doi:10.1109/ictfcen.2016.8052705

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…FEM finds an approximate answer to boundary value problems for partial differential equations. The method takes the total problem area and divides it into a finite amount of elements and uses variation methods to solve the problem by minimizing the error [13][14][15][16]. The proposed model was based on Pade's approximation method and simplified thermal model.…”

Section: Literature Reviewmentioning

confidence: 99%

Core Temperature Estimation for a Lithium ion 18650 Cell

Surya¹,

Marcis

Williamson

2020

Energies

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This paper deals with the estimation of core temperature (Tc) of a Lithium (Li) ion battery using measured ambient and surface temperatures. The temperatures were measured using thermocouples placed at appropriate locations. A second order thermal model was considered for the core temperature (Tc) estimation. A set of coupled linear ordinary differential equations (ODEs) were obtained by applying Kirchhoff’s current and voltage laws to the thermal model. The coupled ODEs were redefined in the discrete state space representation. The thermal model did not account for small changes in surface temperature (Ts). MATLAB/Simulink were used for modelling a Kalman filter with appropriate process and measurement noise levels. It was found that the temperatures closely followed the current patterns. For high currents, Tc dominated the surface temperature by about 3 K. Tc estimation plays a very important role in designing an effective thermal management and maintaining the state of health (SOH) during fast discharges under limits. Most of the battery management system (BMS) applications required Ts as the input to the controller. Hence, an inverse calculation for estimating Ts from known Tc was carried out and found to be reasonably accurate. It was found that the thermal parameter Cs played a major role in the accuracy of Ts prediction and must have low values to minimize errors.

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Section: Literature Reviewmentioning

confidence: 99%

Core Temperature Estimation for a Lithium ion 18650 Cell

Surya¹,

Marcis

Williamson

2020

Energies

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“…The performance of these batteries can be improved by incorporating silicon electrodes [36,134,139,144,147,149,282,283] or reducing degradation from SEI formation [112,126,130] and mechanical stresses [81,82,109,110,114,117]. Safety is promoted by planning strategies against thermal runaway [32,70,73,158,160,168,170] and predicting battery behavior during ultrafast discharge [164][165][166]. Lead-acid batteries are deployed in uninterruptable power systems.…”

Section: Discussionmentioning

confidence: 99%

“…Sangiri et al modeled thermal contact resistances due to imperfect contact between battery components. These become significant heat generation sources at ultrahigh C-rates (10C and above) [165]. Wang et al applied the concept of thermal inertia to Li-ion batteries after ultrafast discharge.…”

Section: Thermal Runawaymentioning

confidence: 99%

Multiphysics modeling of lithium-ion, lead-acid, and vanadium redox flow batteries

Castro

Rosario

Chong

et al. 2021

Journal of Energy Storage

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“…The following simulations and analysis adopt the cell parameters proposed and experimentally identified in [5], [12], [24] and [25]. In particular, the parameters are representative of the dynamics of 18 650 cells with a graphite anode, a LiCoO 2 cathode.…”

Section: B Thermal Modelmentioning

confidence: 99%

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Marelli

Corno

2021

IEEE Trans. Contr. Syst. Technol.

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This brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 • C from the true value in any point of the cell.

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Electro-thermal modeling of Lithium-ion cell for higher discharge rate applications

Cited by 5 publications

References 8 publications

Core Temperature Estimation for a Lithium ion 18650 Cell

Core Temperature Estimation for a Lithium ion 18650 Cell

Multiphysics modeling of lithium-ion, lead-acid, and vanadium redox flow batteries

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

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