Thermal Model Parameter Identification of a Lithium Battery

Nissing, Dirk; Mahanta, Arindam; Sterkenburg, Stefan van

doi:10.1155/2017/9543781

Cited by 9 publications

(3 citation statements)

References 16 publications

(14 reference statements)

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“…Experimental validation was also proposed for this method. The problem of the dependence of the electrical parameters of the heating source was solved in [154], where a numerical method (similar to a LSM) was used to identify the parameters. In order to understand the heating source, a symmetrical (charge/discharge) pulse current test was arranged.…”

Section: Offline Identification Methodsmentioning

confidence: 99%

Lithium Ion Battery Models and Parameter Identification Techniques

2017

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Nowadays, battery storage systems are very important in both stationary and mobile applications. In particular, lithium ion batteries are a good and promising solution because of their high power and energy densities. The modeling of these devices is very crucial to correctly predict their state of charge (SoC) and state of health (SoH). The literature shows that numerous battery models and parameters estimation techniques have been developed and proposed. Moreover, surveys on their electric, thermal, and aging modeling are also reported. This paper presents a more complete overview of the different proposed battery models and estimation techniques. In particular, a method for classifying the proposed models based on their approaches is proposed. For this classification, the models are divided in three categories: mathematical models, physical models, and circuit models.

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Section: Offline Identification Methodsmentioning

confidence: 99%

Lithium Ion Battery Models and Parameter Identification Techniques

2017

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“…In recent years, a great deal of research has been focused on the modeling of Lithium-ion batteries including electrochemical models [3][4][5][6][7][8][9][10][11][12], mechanical models [13][14][15][16][17][18][19][20][21], Battery Thermal Management System/thermal models [22][23][24][25][26][27][28][29][30], and multiphysics models [31][32][33][34][35][36][37] at scales varying from nanolevel to cell level. Such efforts had immense contribution in improving the safety of lithium-ion battery cells.…”

Section: Introductionmentioning

confidence: 99%

Modeling Electric Vehicle’s Battery Module using Computational Homogenization Approach

Shinde

Song

Sahraei

2023

International Journal of Energy Research

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Numerical simulations of heterogeneous structures like battery modules of electric vehicles are challenging due to the various length scales involved in it. Even with the latest computing technology, it is impossible to simulate the crash scene of the full vehicle resolving all length scales. Such hurdles have prevented manufacturers to understand the mechanical response of battery packs in vehicle crash scenarios. In this work, the problem of multiple length scales was solved using the RVE technique based on homogenization theory. An appropriate representative volume element was identified, and a 3D FE model was developed. Classical first-order boundary conditions were used in this research work. The RVE was subjected to several macroscopic deformations, and its response was obtained. The homogenized material properties were computed from the obtained responses, and a material model available in LS-Dyna’s material library was selected and calibrated to describe the nonlinear multiaxial behavior of the homogenized battery module at the macroscale. For validation, the USABC Crush and Drop Tests were simulated for the detailed and homogenized battery modules. The main output of this research is a robust and computationally efficient tool enabling satisfactory integration of a battery pack model to the vehicle for crash simulations, eliminating the need to simulate micro details at macro length scales. This approach significantly reduced the computational cost. For example, for a Drop Test simulation, the homogenized model reduced the simulation time from 40 hours (detailed model) to about 3 minutes, while maintaining a high precision ( R 2 = 0.9871 ) in predicting the load-displacement response. The system level modeling will enable the stakeholders to perform efficient optimization and safety evaluation for full-scale crashworthiness of electric vehicles.

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“…EC (ethylene carbonate) as a co-solvent demonstrated a high exothermic feedback at beginning temperature under 200 • C. The thermal analysis results suggested that the LiPF 6 /FEC (fluoro ethylene carbonate) + TFEP combination could work as a safer electrolyte system in LIB under severe conditions [3]. A least-square parameter identification procedure was applied to a battery cell with the intention of the parameters determination for thermal modeling [4]. Experimental outcomes demonstrated that the simple model with the characterized parameters fits very precisely to the measurements (lower than 1% deviation) and the dynamic temperature discrepancy was lower than 5%.…”

Section: Introductionmentioning

confidence: 99%

Review of Parameter Determination for Thermal Modeling of Lithium Ion Batteries

2018

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This paper reviews different methods for determination of thermal parameters of lithium ion batteries. Lithium ion batteries are extensively employed for various applications owing to their low memory effect, high specific energy, and power density. One of the problems in the expansion of hybrid and electric vehicle technology is the management and control of operation temperatures and heat generation. Successful battery thermal management designs can lead to better reliability and performance of hybrid and electric vehicles. Thermal cycling and temperature gradients could have a considerable impact on the lifetime of lithium ion battery cells. Thermal management is critical in electric vehicles (EVs) and good thermal battery models are necessary to design proper heating and cooling systems. Consequently, it is necessary to determine thermal parameters of a single cell, such as internal resistance, specific heat capacity, entropic heat coefficient, and thermal conductivity in order to design suitable thermal management system.

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Thermal Model Parameter Identification of a Lithium Battery

Cited by 9 publications

References 16 publications

Lithium Ion Battery Models and Parameter Identification Techniques

Lithium Ion Battery Models and Parameter Identification Techniques

Modeling Electric Vehicle’s Battery Module using Computational Homogenization Approach

Review of Parameter Determination for Thermal Modeling of Lithium Ion Batteries

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