State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model

He, Jiangtao; Wei, Zhongbao; Bian, Xiaolei; Yan, Fengjun

doi:10.1109/tte.2020.2994543

Cited by 146 publications

(46 citation statements)

References 41 publications

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“…He et al. employs an improved Lorentzian function to fit the IC curve and sketches the voltage plateau of lithium-ion batteries (such as lithium-iron phosphate batteries), then the peak height, voltage, and area are extracted to calibrate the SOH by a linear model ( He et al., 2020a ). To improve the resilience against noises, Tang et al.…”

Section: Study Of Feature Extractionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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Section: Study Of Feature Extractionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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“…Similar to EIS, this family of methods represents a nondestructive characterisation technique, with the capability to expose measurable effects of battery ageing with respect to individual ageing mechanisms [9]. This capability has been explored with data-driven ICA methods such as [32], where a Lorentzian voltage-current model was formulated to extract parameters directly correlated with SoH, and [33], similarly with a polynomial-type model. Crucially, the SoH models constructed from one cell may be applied to other cells while retaining low (<1.5%) mean absolute error.…”

Section: Eis and Model-based State Of Health Estimationmentioning

confidence: 99%

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rastegarpanah

Hathaway

2021

Energies

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The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

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“…Machine learning-based methods have been progressively exploited to predict SOH due to their strong data processing and nonlinear fitting capabilities [15]. These methods usually employ different machine learning algorithms to capture the degradation trend from measurement and generate healthy features representing SOH variation [16].…”

Section: Introductionmentioning

confidence: 99%

A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning

Shu

Shen

et al. 2021

IEEE Trans. Transp. Electrific.

113

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The application of machine learning-based state of health (SOH) prediction is hindered by large demand of training data. To conquer this defect, a flexible and easy transferred SOH prediction scheme for lithium-ion battery packs is developed. Firstly, the charging duration for a predefined voltage range is hired as the health feature to quantify capacity degradation. Then, the long short-term memory (LSTM) network and transfer learning (TL) with fine-tuning strategy are incorporated to constitute the cell mean model (CMM) for SOH prediction with partial training data. Next, to evaluate the SOH inconsistencies among cells, the LSTM model is employed as the cell difference model (CDM), and the minimum estimation value of CDM is identified to determine pack SOH. The experimental results reveal that even when the first 360 cycle data, occupying only 40% in the whole 904 cycle data, are chosen and constituted to the dataset for model training, the obtained estimation algorithm can still predict SOH precisely with the error of less than 3%, thus remarkably reducing the training data amount and mitigating the computation burden during model training. In addition, the preferable validation results on different types of lithium-ion batteries further manifest the extendibility of the proposed strategy. Index Terms-Lithium-ion battery pack, long short-term memory (LSTM), transfer learning (TL), state of health (SOH).

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State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model

Cited by 146 publications

References 41 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning

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