Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search

Kim, Minho; Chun, Huiyong; Kim, Jungsoo; Kim, Kwangrae; Yu, Jungwook; Kim, Taegyun; Han, Soohee

doi:10.1016/j.apenergy.2019.113644

Cited by 89 publications

(28 citation statements)

References 44 publications

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“…Reference [126] realizes battery SOH prediction based on the gated recurrent unit (GRU). Reference [127] estimates battery parameters based on a deep Bayesian neural network. References [128] uses the deep Gaussian process algorithm to realize the health monitoring of lithium-ion batteries.…”

Section: Neural Network Algorithmmentioning

confidence: 99%

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: Neural Network Algorithmmentioning

confidence: 99%

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

Zhang

et al. 2020

WEVJ

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“…Considering the battery modelling, various categories can be achieved to simulate the dynamic characteristics including electrochemical models [21], equivalent circuit models and data-driven models [22], while the computational effectiveness for further promotion is still of essential for cloud-control. Equivalent circuit models (ECMs) are one of the widely prevalent models applied in embedded BMS, and the effectiveness for online estimation delivers the promotion on future cloud-controlling.…”

Section: A Second-order Equivalent Circuit Modelmentioning

confidence: 99%

“…Herein, the minimum and maximum of each region are determined from the fluctuating database, and the numbers of the regions are divided as artificial selection. Additionally, the amount of information I can be defined as equation (22). It is worthy to mention that the probability is generally less than one, thus the logarithm of the probability is negative which is hard to interpret the content of information entropy.…”

Section: B) Information Entropy Theorymentioning

confidence: 99%

A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

et al. 2020

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Considered to be the state-of-art solution for intelligent management of electric vehicles, cloud-control has been broadly investigated especially for parameterization and state estimation. Considering the operational cloud-database, the sampling intervals contribute to the precision and robustness of the battery management, and a balance between storage and performance is of crucial importance for real-time controlling. Unfortunately, the comprehensive performances on variable sampling intervals are doubtful for the development of cloud-control. Herein, the research of sampling intervals is carried out and the operational applications are simulated for validation including the precision, robustness and information content. 5 th -order spherical simplex-redial Kalman filter and particle swarm optimizationsimulated annealing methods are developed for researching the influences on precision of state of charge (SOC) estimation and parameterization under stable or dynamic conditions. Moreover, the information content of the desecrated database is evaluated based on the information entropy. The comparative results are carried out for separate characteristic, and the analysis exhibit the special performances for diverse sampling interval. The research confirms the differences on diverse intervals on operational applications, and the analysis might deliver effective guidance for future processing framework based on cloudcontrolling towards specific intentions.

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“…Many studies have been conducted to accurately estimate the parameters of the electrochemical lithium-ion battery model. Some of these are based on Jacobian-based algorithms such as the Gauss-Newton method or Levenberg-Marquardt method, which have the advantage of fast convergence within a few iterations [8], [9]. However, since the electrochemical lithium-ion battery model is highly nonlinear, the Jacobianbased parameter estimation algorithms are likely to converge to the local optima and hence show the poor parameter estimation accuracy [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Some of these are based on Jacobian-based algorithms such as the Gauss-Newton method or Levenberg-Marquardt method, which have the advantage of fast convergence within a few iterations [8], [9]. However, since the electrochemical lithium-ion battery model is highly nonlinear, the Jacobianbased parameter estimation algorithms are likely to converge to the local optima and hence show the poor parameter estimation accuracy [9], [10]. Empirical studies have also been conducted to estimate the parameters of the electrochemical lithium-ion battery model using meta-heuristic algorithms such as the genetic algorithm, particle swarm optimization, and harmony search [11]- [16].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Parameter Estimation of an Electrochemical Lithium-Ion Battery Model Using a Long Short-Term Memory Network

Chun

Kim

et al. 2020

IEEE Access

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An electrochemical lithium-ion battery model is well known to be suited for effectively describing the microstructure evolution in charging and discharging processes of a lithium-ion battery with practically realizable complexity. This paper presents a neural network-based parameter estimation scheme to identify the parameters of an electrochemical lithium-ion battery model in a near-optimal and real-time manner in order to consistently observe the electrochemical states of batteries. The network is first trained to learn the dynamics of the electrochemical lithium-ion battery model, and then, it is applied to estimate the parameters with available finite-time measurements of voltage, current, temperature, and state of charge. In order to efficiently learn the dynamic characteristics of a lithium-ion battery, a well-known recurrent neural network, called a long short-term memory model, is employed with other techniques such as batch normalization, dropout, and stochastic gradient descent with warm restarts for learning speed enhancement and regularization. Using synthetic and experimental data, we show that the proposed estimation scheme works well, finding parameters and recovering the voltage profiles within the root-mean-square error of 0.43% and 26 mV, respectively, even with measurements obtained within a sufficiently short interval of time. INDEX TERMS Electrochemical battery model, lithium-ion battery, long short-term memory, real-time parameter estimation, recurrent neural network, synthetic data generation.

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Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search

Cited by 89 publications

References 44 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 Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

Real-Time Parameter Estimation of an Electrochemical Lithium-Ion Battery Model Using a Long Short-Term Memory Network

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