Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

Zhang, Liqiang; Wang, Lixin; Hinds, Gareth; Lyu, Chao; Zheng, Jun; Li, Junfu

doi:10.1016/j.jpowsour.2014.07.110

Cited by 167 publications

(81 citation statements)

References 22 publications

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“…In order to validate the diffusion model with experimental data, a 1D model is set up to simulate the dynamic stress tests (DST) as described in [17]. In the model, the cathode has a thickness of 1.6 μm.…”

Section: Validation Of LI Ion Diffusion Modelmentioning

confidence: 99%

Three-dimensional phase field based finite element study on Li intercalation-induced stress in polycrystalline LiCoO2

Zhang

Jung

et al. 2015

Journal of Power Sources

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In this study, the stress generation of LiCoO2 with realistic 3D microstructures has been studied systematically. Phase field method was employed to generate the 3D microstructures with different grain sizes. The effects of grain size, grain crystallographic orientation, and grain boundary diffusivity on chemical diffusion coefficient and stress generation were studied using finite element method. The calculated chemical diffusion coefficient is about in the range of 8.5×10 -10 cm 2 /s to 3.6×10 -9 cm 2 /s. Stresses increase with the increase of grain size, due to more accumulation of Li ion near the grain boundary regions in larger grain size systems, which causes a larger concentration gradient. Failure is more likely to occur in large grain systems. The chemical diffusion coefficients increase with increasing grain orientation angle irrespective of grain boundary diffusivity, due to alignment of global Li ion diffusion path with high grain orientations. Grain boundary diffusivity has opposite effect on the hydrostatic stress. As small grain boundary diffusivity, the stress increases with increasing grain orientation angle, due to

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Section: Validation Of LI Ion Diffusion Modelmentioning

confidence: 99%

Three-dimensional phase field based finite element study on Li intercalation-induced stress in polycrystalline LiCoO2

Zhang

Jung

et al. 2015

Journal of Power Sources

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“…According to our previous study [28,29], the parameters of the multi-physics model can be identified more correctly using the dynamic charge/discharge data at two different ambient temperatures than those using a simple operating condition at only one ambient temperature.…”

Section: Methodsmentioning

confidence: 99%

“…These parameters have been obtained from battery specifications, literature or measured directly after disassembling real batteries. In this article, the parallelized multi-objective genetic algorithm (MOGA) [29,36] was used to identify those model parameters. The effectiveness of the proposed identification method was proven in [28,36] by using the RPT and the "synthetic experiment", which is acquired from model simulation with known parameters instead of a real experiment, and the averaged relative error is less than 10%.…”

Section: Parameter Identificationmentioning

confidence: 99%

Non-Destructive Analysis of Degradation Mechanisms in Cycle-Aged Graphite/LiCoO2 Batteries

et al. 2014

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Abstract:Non-destructive analysis of degradation mechanisms can be very beneficial for the prognostics and health management (PHM) study of lithium-ion batteries. In this paper, a type of graphite/LiCoO2 battery was cycle aged at high ambient temperature, then 25 parameters of the multi-physics model were identified. Nine key parameters degraded with the cycle life, and they were treated as indicators of battery degradation. Accordingly, the degradation mechanism was discussed by using the multi-physics model and key parameters, and the reasons for capacity fade and the internal resistance increase were analyzed in detail. All evidence indicates that the formation reaction of the solid electrolyte interface (SEI) film is the main cause of battery degradation at high ambient temperature.

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“…[8][9][10][11][12][13][14][15] In this case, the collected experimental data must be processed with optimization methods in order to reveal unknown parameters and properties. A literature review of the methods applied to Li-ion batteries, i.e., the Parameter Estimation methods (PE), is provided in the first part of this study.…”

Section: 7mentioning

confidence: 99%

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Rajabloo

Jokar

Désilets

et al. 2016

J. Electrochem. Soc.

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This paper is the second part of a two part study on parameter estimation of Li-ion batteries. The methodology was developed in Part I. In Part II, the methodology is tested for LiCoO 2 , LiMn 2 O 4 and LiFePO 4 positive electrode materials. An inverse method combined to a simplified version of the Pseudo-two-Dimensional (P2D) model is used to identify the solid diffusion coefficients (D s,n and D s,p ), the intercalation/deintercalation reaction-rate constants (K n and K p ), the initial SOC (SOC n,0 and SOC p,0 ), and the electroactive surface areas (S n and S p ) of Li-ion batteries. Experimental cell potentials for both low and high discharge rates provide the reference data for minimizing the objective function in the best time interval. For all cases simulated, the numerical predictions show excellent agreement with the experimental data. Lithium-ion (Li-ion) batteries are increasingly employed for energy storage. Their working voltage and energy density are higher than those of similar energy storage technologies. Their service life is longer. They exhibit high energy-to-weight ratios and low selfdischarge. As a result, they have become the preferred energy storage devices in the electronics and the automotive industries.Mathematical modeling of Li-ion batteries is an essential engineering tool for their design and operation. Two different approaches are usually adopted to predict their behavior. These approaches may be divided, broadly speaking, into empirical models and electrochemical models.Empirical models are the simplest mathematical models. They are relatively easy to implement and they provide fast responses. This is why they are mostly suited for control systems used in the high tech industry and in the automotive industry. The scope of applications of empirical models is however narrow. Empirical models ignore the physical phenomena that take place in the cell. Consequently, they cannot predict the life and the capacity fading of the battery. Furthermore, they are only valid for the battery for which they have been developed. [1][2][3] Electrochemical models provide, on the other hand, reliable responses of the battery under a wide range of operating conditions and for different applications. They account for the chemical/ electrochemical kinetics and the transport phenomena. Electrochemical models are unequivocally superior to empirical models. But they are also more complex and require longer computation times.Among the electrochemical models, the Pseudo-two-Dimensional (P2D) model stands out. The P2D model rests on the porous electrode theory, the concentrated solution theory and the use of appropriate kinetics equations.4-6 A simplified and computationally efficient version of the P2D model is the Single Particle Model (SPM). In the SPM, it is assumed that the current distribution along the thickness of the porous electrode remains uniform and that the electrolyte properties are constant. 5,7Both empirical and electrochemical models need to be calibrated in order to simulate faithfully the ...

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Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

Cited by 167 publications

References 22 publications

Three-dimensional phase field based finite element study on Li intercalation-induced stress in polycrystalline LiCoO2

Three-dimensional phase field based finite element study on Li intercalation-induced stress in polycrystalline LiCoO2

Non-Destructive Analysis of Degradation Mechanisms in Cycle-Aged Graphite/LiCoO2 Batteries

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

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