Computational modeling of Li-ion batteries

Grazioli, Davide; Magri, M.; Salvadori, Alberto

doi:10.1007/s00466-016-1325-8

Cited by 67 publications

(49 citation statements)

References 276 publications

(418 reference statements)

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“…The total overpotential is induced by various processes occurring in a battery, such as the charge-transfer reactions at the electrode/electrolyte interfaces ( ) [64], the diffusion and migration of Li-ions across the electrolyte ( ) [65], diffusion and migration of Li-ions in the electrodes ( ) [66] and Ohmic losses ( ) [67]. Therefore, the total battery overpotential can also be expressed as…”

Section: Heat Generationmentioning

confidence: 99%

A review on various temperature-indication methods for Li-ion batteries

et al. 2019

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Temperature measurements of Li-ion batteries are important for assisting Battery Management Systems in controlling highly relevant states, such as State-of-Charge and State-of-Health. In addition, temperature measurements are essential to prevent dangerous situations and to maximize the performance and cycle life of batteries. However, due to thermal gradients, which might quickly develop during operation, fast and accurate temperature measurements can be rather challenging. For a proper selection of the temperature measurement method, aspects such as measurement range, accuracy, resolution, and costs of the method are important. After providing a brief overview of the working principle of Li-ion batteries, including the heat generation principles and possible consequences, this review gives a comprehensive overview of various temperature measurement methods that can be used for temperature indication of Li-ion batteries. At present, traditional temperature measurement methods, such as thermistors and thermocouples, are extensively used. Several recently introduced methods, such as impedance-based temperature indication and fiber Bragg-grating techniques, are under investigation in order to determine if those are suitable for largescale introduction in sophisticated battery-powered applications.

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Section: Heat Generationmentioning

confidence: 99%

A review on various temperature-indication methods for Li-ion batteries

et al. 2019

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“…Even when the deformation of cathode materials is small, e.g. LiCoO 2 , LiMn 2 O 3 and LiFePO 4 , the cyclic lithiation and delithiation of active particles leads to cracks and loss of contact with the matrix, which gradually results in a capacity loss and eventually failure of the battery [17,18]. Hence, to design a longer lifetime and higher energy density batteries, simulation of coupled diffusion-mechanics is of primary importance [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the work done in the literature on the simulation of coupled diffusionmechanics in batteries is based on the pioneering works of Larché and Cahn [21], in which a framework for solid-state diffusion was developed for compositional changes in the solid state [5]. In general, due to its multiphysics and multiscale nature, the simulation of lithium ion batteries is a challenging task [17]. Analytical methods for the solution of coupled diffusion-mechanics problems are limited to simple geometrical shapes [22], therefore approximate solutions using numerical techniques such as finite elements are often required [17].…”

Section: Introductionmentioning

confidence: 99%

Enriched continuum for multi-scale transient diffusion coupled to mechanics

Waseem

Heuzé

Stainier

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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In this article, we present a computationally efficient homogenization technique for linear coupled diffusion-mechanics problems. It considers a linear chemo-mechanical material model at the fine scale, and relies on a full separation of scales between the time scales governing diffusion and mechanical phenomena, and a relaxed separation of scales for diffusion between the matrix and the inclusion. When the characteristic time scales associated with mass diffusion are large compared to those linked to the deformation, the mechanical problem can be considered to be quasi-static, and a full separation of scales can be assumed, whereas the diffusion problem remains transient. Using equivalence of the sum of virtual powers of internal and transient forces between the microscale and the macroscale, a homogenization framework is derived for the mass diffusion, while for the mechanical case, considering its quasi-static nature, the classical equivalence of the virtual work of internal forces is used instead. Model reduction is then applied at the microscale. Assuming a relaxed separation of scales for diffusion phenomena, the microscopic fields are split into steady-state and transient parts, for which distinct reduced bases are extracted, using static condensation for the steady-state part and the solution of an eigenvalue problem for the transient part. The model reduction at the microscale results in emergent macroscopic enriched field variables, evolution of which is described with a set of ordinary differential equations which are inexpensive to solve. The net result is a coupled diffusion-mechanics enriched continuum at the macroscale. Numerical examples are conducted for the cathode-electrolyte system characteristic of a lithium ion battery. The proposed reduced order homogenization method is shown to be able to capture the coupled behavior of this system, whereby high computational gains are obtained relative to a full computational homogenization method.

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“…Sheikhan et al [10] used an artificial neural network to realize feedback correction of the traditional time-integration method: Considering the aging effect of power battery, they used an optimized multi-layer structure perceptron and radial basis function (RBF) neural network as corrective measures of the coulomb counting method to achieve accurate estimation of SOC. Wei et al [11] used the neural network model to estimate SOC and improved the estimation accuracy through the Kalman filter.The model-based SOC estimation method is the most popular method in recent years [12]. It can be divided into two categories: Electrochemical and equivalent circuit.The advantage of the electrochemical model is that it can reflect the internal mechanism of the battery.…”

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confidence: 99%

“…The model-based SOC estimation method is the most popular method in recent years [12]. It can be divided into two categories: Electrochemical and equivalent circuit.…”

mentioning

confidence: 99%

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Liu

et al. 2019

Energies

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State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.The intrinsic parameter measurement method is to select suitable battery parameters that can directly characterize the SOC for direct measurement, and then estimate the battery SOC through these parameters. The commonly used intrinsic parameters include open circuit voltage (OCV), impedance spectrum [7], electromotive force (EMF), and internal resistance (IR). The advantage of this method is that the battery SOC can be directly obtained through the mapping relationship between the SOC and the battery intrinsic parameter, and the disadvantage is that the estimation accuracy relies excessively on the accuracy of the mapping relationship between SOC and intrinsic parameters of battery. Holger [8] summarized the SOC estimation method based on the impedance spectrum. Waag [9] confirmed that the impedance parameter of a Li-ion battery changes negatively as the battery ages.Computer intelligence methods found in literature mainly include the neural network model, genetic algorithm and particles swarm optimization. The advantage of these methods is that they do not require a thorough understanding of battery internal mechanism, but they do require large amounts of data for the machine learning process. Sheikhan et al. [10] used an artificial neural network to realize feedback correction of the traditional time-integration m...

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Computational modeling of Li-ion batteries

Cited by 67 publications

References 276 publications

A review on various temperature-indication methods for Li-ion batteries

A review on various temperature-indication methods for Li-ion batteries

Enriched continuum for multi-scale transient diffusion coupled to mechanics

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

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