Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

Santhanagopalan, Shriram; Guo, Qingzhi; White, Ralph E.

doi:10.1149/1.2422896

Cited by 128 publications

(97 citation statements)

References 36 publications

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“…In addition to calendar age (t age ) and cycle number (N) which are known to cause degradation in proportion to t z [13] and N [14], respectively; temperature [4,23], SoC [24], ∆SoC [25] and current [26] are also known accelerators of battery degradation. The effects of these stress factors-based on published literature-at the microscopic level along with their quantifications in the P2D model are presented using flow charts below.…”

Section: Expected Parameter Changes Resulting From Battery Degradationmentioning

confidence: 99%

“…Based a number of recent studies [14,20,21], refined parameter set presented in the second column of Table 2 that is used as an initial guess for the optimisation process. The potential U´of the graphite (Li x C 6 ) negative electrode is assumed to initially follow the empirical correlation from [40].…”

Section: Verifying the Non-aged Refined Parameter Set For Linicoalomentioning

confidence: 99%

“…Parameterisation of higher fidelity models such as the single particle (SP) model with various dynamic and thermal extensions has also been undertaken using various system identification methods [13]. Santhanagopalan et al [14] for example used the Levenberg-Marquardt optimization algorithm to identify a subset of five parameters for both the pseudo two-dimensional (P2D) porous electrode model and SP models using constant charge and discharge cycles. An important contribution to parameter identification was made by Forman et al [15] who identified the full set of parameters (88 scalars and function control points) of the P2D model using a genetic algorithm derived from the drive-cycle data from a plug-in hybrid electric vehicle (PHEV).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Perera²,

et al. 2016

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Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations.

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Section: Expected Parameter Changes Resulting From Battery Degradationmentioning

confidence: 99%

Section: Verifying the Non-aged Refined Parameter Set For Linicoalomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Perera²,

et al. 2016

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“…Facet 1 of cell 1-a is contacted with L-bracket board b1, and the total heat flow rate on this surface is expressed as [18] where T 1Àa denotes the temperature of cell 1-a and T b1 denotes the temperature of board b1. Facet 3 of cell 1-a is contacted with cell 1-b, so the total heat flow rate on this boundary is…”

Section: Mathematical Modelmentioning

confidence: 99%

Thermal Model for Lithium Ion Battery Pack with Mixed Parallel and Series Configuration

White

2011

J. Electrochem. Soc.

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In this work, a mathematical thermal model for lithium ion battery pack with specific configuration was developed by coupling the single particle model and energy balance equation with basic circuit constraints. The temperature variation at different parts of the battery pack was considered in charge/discharge operations, and the dependency of cell parameters on temperature were taken into account. The model was validated by comparing the simulated current, voltage, and temperature profiles with experimental data. Case studies such as battery balancing and circuit interruption were also performed and discussed.

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“…In order to overcome the difficulties of experimental measurements, various research strategies for parameter estimation (PE) have been deployed. Table I summarizes the characteristics of the Parameter Estimation (PE) studies that have been applied to Li-ion batteries.In 2004, a PE study of a polymer electrolyte membrane fuel cell (PEMFC) cathode was presented by Guo et al11 Based on this research, five internal parameters of the Li-ion battery were estimated by Santhanagopalan et al in 2007. These parameters are the diffusivity of Li + ions in the positive electrode (D s,p ), the reaction rate constants at the electrodes/electrolyte interface (K n and K p ) and the initial State Of Charge of negative and positive electrodes (SOC n,0 and SOC p,0 ).…”

mentioning

confidence: 99%

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

Jokar

Rajabloo

Désilets

et al. 2016

J. Electrochem. Soc.

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An electrochemical Parameter Estimation (PE) study of lithium-ion batteries for different materials is presented. The PE methodology is developed in Part I of the study and the challenges on the different materials for the positive electrode including LiCoO 2 , LiMn 2 O 4 and LiFePO 4 are examined in Part II. The most influential electrochemical parameters of the Li-ion battery are estimated by means of an inverse method. The inverse method rests on five elements: the input parameters, a direct model, the reference data, an objective function and an optimizer. Eight electrochemical variables are considered as the target of the PE study. A simplified version of Pseudo-two-Dimensional (P2D) model is developed for the direct model. The P2D model predictions coupled to a random noise function are employed to generate the reference data. The data include the cell potential values with respect to the battery capacity at low and high discharge rates. The least-squared function and Genetic Algorithm are employed as the objective function and its optimizer, respectively. The best time domain for the estimation of each parameter is calculated by using a sensitivity analysis performed for different discharge curves. Results show that the methodology remains accurate and stable at both low and high discharge rates. The simultaneous high power and energy density of Lithium-ion (Li-ion) batteries have made it the preferred device for storing electricity. As a result, Li-ion batteries are increasingly used in various applications including electronics and the automotive industry. Regardless of the shape and of the battery pack arrangement, the internal structure of the battery usually comprises four main components: Two electrodes, positive and negative, an electrolyte, and a separator. These components are made of materials that have been gradually modified over the years so as to improve the efficiency, the safety and the performance of the batteries and to reduce their cost. 1-3A Battery Management System (BMS) is crucial for monitoring the operation of the battery pack. The BMS must interact with all the elements of the system in order to control it and to protect the Li-ion cells. The intelligence of the BMS is based on a mathematical model that simulates and predicts the different operating conditions of the Li-ion battery pack. In high tech and automotive industries, the BMS usually relies on empirical-based models. These models are simple and provide fast response. They cannot however predict the performance of the battery as it ages. Moreover, they are only applicable to a specific cell, i.e., they cannot be transposed to other battery packs without recalibration. [4][5][6] Electrochemical-based models of Li-ion batteries, on the other hand, overcome these shortcomings. These models rest on chemical/electrochemical kinetics and transport equations. These Li-ion battery models are more complicated and CPU time-consuming than empirical based models. They are, on the other hand, more versatile and they provide reliable a...

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Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

Cited by 128 publications

References 36 publications

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Thermal Model for Lithium Ion Battery Pack with Mixed Parallel and Series Configuration

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

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