Experimental identification and validation of an electrochemical model of a lithium-ion battery

Speltino, C.; Domenico, Domenico Di; Fiengo, Giovanni; Stefanopoulou, Anna G.

doi:10.23919/ecc.2009.7074544

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…The range of every kinetic parameter presented in Table is obtained by gathering different values for every parameter from the literature and setting the minimum and maximum value as the boundary of each kinetic parameter. Within the range, every parameter can be expressed on a linear or a logarithmic scale, ie,

p_{i} = β \underline{p_{i}} + false(1 - β false) \overset{p_{i}}{true},

normall normalo normalg p_{i} = β normall normalo normalg \underline{p_{i}} + false(1 - β false) normall normalo normalg \overset{p_{i}}{true},

where β ∈[0,1], p i ∈ p denotes every kinetic parameter, p i _ and

\overset{p_{i}}{true}

represent minimum and maximum value of the parameter, respectively.…”

Section: Parameters Grouping and Rangingmentioning

confidence: 99%

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Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

et al. 2018

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Lithium-ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium-ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two-step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two-step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low-current discharges, while the second group contains kinetic parameters, which are estimated using a well-designed highly-dynamic pulse (dis-)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium-ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.How to cite this article: Jin N, Danilov DL, Van den Hof PMJ, Donkers MCF. Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis. Int J Energy Res.

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p_{i} = β \underline{p_{i}} + false(1 - β false) \overset{p_{i}}{true},

normall normalo normalg p_{i} = β normall normalo normalg \underline{p_{i}} + false(1 - β false) normall normalo normalg \overset{p_{i}}{true},

where β ∈[0,1], p i ∈ p denotes every kinetic parameter, p i _ and

\overset{p_{i}}{true}

represent minimum and maximum value of the parameter, respectively.…”

Section: Parameters Grouping and Rangingmentioning

confidence: 99%

“…The idea of estimating only a subset of the parameter set in the DFN model has been proposed before in other works . In particular, all the aforementioned papers propose to estimate some of the parameters, see Table , while keeping the others at some nominal value.…”

Section: Kinetics Modellingmentioning

confidence: 99%

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

et al. 2018

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“…The objective function/fitness function used for the parameter identification method via particle swarm optimization (PSO) algorithm is given by the following function [17,28]:…”

Section: Parameter To Identifymentioning

confidence: 99%

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Anwar

Izadian

2017

Energies

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Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO 2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

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“…The initialized value of the parameters to identify were taken from some well guessed values based on published literatures [32] [59] and provided in Table 6.1. All of these values were adopted while initializing the particle swarm optimization (PSO) algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…All of these values were adopted while initializing the particle swarm optimization (PSO) algorithm. The objective function/fitness function for this parameter optimization technique is the following [32]:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

Rahman¹,

Anwar²,

Izadian³

2016

Adv Automob Eng

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Experimental identification and validation of an electrochemical model of a lithium-ion battery

Cited by 22 publications

References 11 publications

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

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