Parameter identification of an electrochemical lithium-ion battery model with convolutional neural network

Chun, Huiyong; Kim, Jungsoo; Han, Soohee

doi:10.1016/j.ifacol.2019.08.167

Cited by 37 publications

(14 citation statements)

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Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

“…References [144,145] analyze the aging of the battery by forming an electrochemical model. Reference [146] combines the electrochemical model with CNN to judge the state of some parameters (such as solid By establishing an electrochemical model, we can simulate the internal working state of the battery to determine some electrochemical parameters that we cannot directly measure. References [144,145] analyze the aging of the battery by forming an electrochemical model.…”

Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

“…References [144,145] analyze the aging of the battery by forming an electrochemical model. Reference [146] combines the electrochemical model with CNN to judge the state of some parameters (such as solid particle conductivity, solid particle areas, and solid electrolyte interface layer thickness) inside the battery. Owing to relatively few sorting methods based on the material chemistry of the battery, we can conduct in-depth studies in this regard and find more suitable electrochemical parameters for sorting batteries.…”

Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

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Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.

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Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

“…References [144,145] analyze the aging of the battery by forming an electrochemical model. Reference [146] combines the electrochemical model with CNN to judge the state of some parameters (such as solid particle conductivity, solid particle areas, and solid electrolyte interface layer thickness) inside the battery. Owing to relatively few sorting methods based on the material chemistry of the battery, we can conduct in-depth studies in this regard and find more suitable electrochemical parameters for sorting batteries.…”

Section: Sorting Methods Based On Materials Chemistrymentioning

confidence: 99%

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Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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“…In terms of adaptive methods based on algorithms, there are some other approaches based on time series [14] or fractional analysis [15]. Other widely used approaches are those of genetic-algorithm-based optimization, which was developed by Ramos et al [16], or neural-network-based approaches, which were presented by Chun et al [17]. From all of the mentioned options, it is necessary to identify the methods that can be directly implemented during real operations in stationary applications in order to avoid interfering in the operating mode, as suggested by Shahriari and Farrokhi [18].…”

Section: Introductionmentioning

confidence: 99%

A Battery Management System with EIS Monitoring of Life Expectancy for Lead–Acid Batteries

Olarte

Ilarduya²,

Zulueta

et al. 2021

Electronics

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This work presents a battery management system for lead–acid batteries that integrates a battery-block (12 V) sensor that allows the online monitoring of a cell’s temperature, voltage, and impedance spectra. The monitoring and diagnostic capabilities enable the implementation of improved battery management algorithms in order to increase the life expectancy of lead–acid batteries and report the battery health conditions. The novelty is based on the online monitoring of the evolution of electrochemical impedance spectroscopy (EIS) over a battery’s life as a way to monitor the battery’s performance. Active cell balancing is also proposed as an alternative to traditional charge equalization to minimize excessive electrolyte consumption. A battery-block sensor (VTZ) was validated by using the correlation between experimental data collected from electrochemical impedance spectroscopy lab-testing equipment and sensors that were implemented in a series of 12 V lead–acid battery blocks. The modular design and small size allow easy and direct integration into different commercial cell formats, and the proposed methodology can be used for applications ranging from automotive to stationary energy storage.

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“…Several studies have been conducted for parameter identification of the electrochemical battery models [1]- [8]. Although an electrochemical model is very helpful for observing physical phenomena inside a Li-ion battery, its parameter identification essentially needs time-consuming and computationally demanding techniques such as machine learning or meta-heuristic algorithms because electrochemical models consist of complicated nonlinear partial differential equations with several boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix

Kim

Han

2020

IEEE Access

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Monitoring the state of health (SOH) for Li-ion batteries is crucial in the battery management system (BMS), for their efficient and safe use. Due to time-varying battery parameters and insufficient computation capability of the BMSs, computationally efficient online parameter identification is practically required. So, a simple equivalent circuit model (ECM) based recursive least squares (RLS) parameter identification algorithm has been widely used. However, it has long been acknowledged that this algorithm suffers from wind-up problem when the input current doesn't provide sufficient excitation. It causes numerical instability and then induces large sensitivity of identified parameter values to the noise or truncation error of sensor data, leading to large parameter identification errors. In this work, a new reliable version of ECM based RLS, called a condition number based recursive least squares (CNRLS) algorithm, is proposed to avoid large errors due to insufficient excitation by monitoring the condition number of the error covariance matrix If the condition number is greater than a certain prescribed value, currently identified parameters are considered unreliable and hence the proposed algorithm uses stored internal variables previously computed with sufficiently exciting input current, leading to small condition number of the error covariance matrix. Accordingly, the forgetting factor is also adjusted to give a larger weight to such stored internal variables in order to overcome the insufficient excitation of the input current. It is shown with a1-RC equivalent circuit model that the proposed CNRLS algorithm is more noise-tolerant and accurate than two benchmarks including the standard RLS and adaptive forgetting factor RLS (AFFRLS) in terms of mean absolute errors, with almost the same computing cost.

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Parameter identification of an electrochemical lithium-ion battery model with convolutional neural network

Cited by 37 publications

References 14 publications

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

A Battery Management System with EIS Monitoring of Life Expectancy for Lead–Acid Batteries

Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix

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