State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Chemali, Ephrem; Kollmeyer, Phillip J.; Preindl, Matthias; Emadi, Ali

doi:10.1016/j.jpowsour.2018.06.104

Cited by 538 publications

(204 citation statements)

References 27 publications

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“…Table 10 presents some important papers in this field. [69] introduced a machine learning methodology for state of charge (SOC) estimation in Li-ion batteries utilizing deep neural networks. In this study, a new approach utilizing deep neural networks was presented for estimating battery SOC.…”

Section: Deep Learningmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Deep Learningmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…In PSO, acceleration coefficients c 1 , c 2 , and weight factor w were assigned to 2 and 0.5, respectively. The hyperparameters of BPNN 39 , RBFNN 40 , ELM 41 , DRNN 13 , and RF 42 (2020) 10:4687 | https://doi.org/10.1038/s41598-020-61464-7…”

Section: Experiments and Data Development A Test Bench Model Was Estmentioning

confidence: 99%

“…SOC estimation of lithium-ion batteries is commonly estimated using three methods, namely, conventional 8,9 , model-based [10][11][12] , and machine learning (ML) approaches [13][14][15] . Conventional approaches are simple but are unsuitable for online operations 16 .…”

mentioning

confidence: 99%

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan

Lipu

Hussain

et al. 2020

Sci Rep

168

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State of charge (Soc) is a crucial index used in the assessment of electric vehicle (eV) battery storage systems. thus, Soc estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise Soc assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced Soc estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced Soc estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

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“…More in precise, the model-based estimation with adaptive Kalman and particle filters or observers and fuzzy logic [40][41][42] or machine learning algorithms such as artificial neural networks (ANN) [43,44] and support vector machines (SVM) [45] are typically used for on-board implementation, taking into account their increased computational and memory requirements. On the other hand, the SoC estimation from Ah-counting [46] depends on the accuracy (sampling precision and frequency) of the current sensors and the initialization of the cell's capacity.…”

Section: In Discrete-time Domainmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Cited by 538 publications

References 27 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

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