Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks

Eddahech, Akram; Briat, Olivier; Bertrand, Nicolas; Delétage, Jean-Yves; Vinassa, Jean-Michel

doi:10.1016/j.ijepes.2012.04.050

Cited by 364 publications

(155 citation statements)

References 28 publications

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“…Methods such as the AutoRegressive (AR) model [12], neural network [13][14][15][16][17][18], support vector machine (SVM) [19][20][21][22], and relevance vector machine (RVM) [23][24][25][26][27][28][29][30][31][32][33] are used.…”

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

155

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Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

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Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

155

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“…During the rest time, the acidic impurities generated at the positive electrode can destroy the SEI layer at the negative and liberate lithium ions [37,38]. Eddahech et al [39] pointed out that recovery phenomenon may be related to charge redistribution when there is no charge or discharge force acting on them. Baghdadi et al [40] argued that a rearrangement of the lithium within the active material crystalline structure occurs thanks to solid diffusion during the battery rest.…”

Section: The Regeneration Phenomenon In Battery Degradationmentioning

confidence: 99%

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

et al. 2016

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Abstract:State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

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“…In the aging stage of batteries used in EVs (i.e., capacity loss <20%), the capacity loss (Qloss) is mainly caused by the loss of lithium inventory with the formation and thickening of the solid electrolyte interphase (SEI) film [4,22], so the capacity loss will be accompanied by the SEI film resistance (RSEI) increase.…”

Section: The Relationship Between Resistance Increase and Capacity Lossmentioning

confidence: 99%

“…For internal resistance identification, the most basic methods are performed by fitting the measured data of the hybrid pulse power characteristic (HPPC) tests or the electrochemical impedance spectroscopy (EIS) tests in least-squares sense under off-line operation conditions [3,4]. In addition, the results of these off-line identifications are always taken as reference values for on-line methods or training data for battery modelling.…”

Section: Introductionmentioning

confidence: 99%

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Wang¹,

Pei²,

Wang

et al. 2015

Energies

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Abstract:A state-of-health (SOH) estimation method for electric vehicles (EVs) is presented with three main advantages: (1) it provides joint estimation of cell's aging states in terms of power and energy (i.e., SOHP and SOHE)-because the determination of SOHP and SOHE can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2) it has wide applicability to various ambient temperatures-to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3) it needs low computational efforts for on-board application-based on a linear equation of cell's dynamic behaviors, the recursive least-squares (RLS) algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

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Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks

Cited by 364 publications

References 28 publications

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

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