A Deep Learning Method with Ensemble Learning for Capacity Estimation of Lithium-ion Battery

Cao, Mengda; Zhang, Tao; Wang, Yu; Zhang, Shihui; Liu, Huan

doi:10.1109/phm-shanghai49105.2020.9280981

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Machine learning is an artificial intelligence algorithm that can effectively analyze big data and discover its value [ 14 ]. In recent years, a lot of research has focused on using data-driven methods to improve the accuracy of SOH estimation [ 15 ]. The data-driven method can avoid the dependence on the battery aging mechanism and the battery model.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

Xie

et al. 2022

Sensors

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In the era of big data, using big data to realize the online estimation of battery SOH has become possible. Traditional solutions based on theoretical models cannot take into account driving behavior and complicated environmental factors. In this paper, an approximate SOH degradation model based on real operating data and environmental temperature data of electric vehicles (EVs) collected with a big data platform is proposed. Firstly, the health indicators are extracted from the historical operating data, and the equivalent capacity at 25 °C is obtained based on the capacity–temperature empirical formula and the capacity offset. Then, the attenuation rate during each charging and discharging process is calculated by combining the operating data and the environmental temperature. Finally, the long short-term memory (LSTM) neural network is used to learn the degradation trend of the battery and predict the future decline trend. The test results show that the proposed method has better performance.

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Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

Xie

et al. 2022

Sensors

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show abstract

“…The method has become more and more popular for its flexibility and the great nonlinear curve fitting capability. Those methods include linear regression (LR) [8], support vector machine (SVM) [9], Gaussian process regression (GPR) [10], artificial neural network (ANN) [11] and some deep-learning algorithms [12]. Contrastingly, the historical operating data before the decommissioning interface of the retired battery is usually unknown and the aging characteristics of second-use batteries are different due to different operating conditions, which bring difficulties to the capacity estimation of the battery.…”

Section: Introductionmentioning

confidence: 99%

Study on Capacity Estimation Methods of Second-Life Application Batteries

Zhang

Wang

et al. 2021

WEVJ

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For the capacity estimation problem of cells in series-retired battery modules, this paper proposed three different methods from the perspective of data-driven, battery curve matching and recession characteristics for different applications. Firstly, based on the premise that the battery history data are available, the features of the IC curve are selected as input for the linear regression models. To avoid multicollinearity among features, we apply a filter-based feature selection method to eliminate redundant features. The results show that the average errors with Multiple Linear Regression are within 1.5%. Secondly, for the situation with a lack of historical operating data, the battery-curve-matching-based method is proposed based on the Dynamic Time Warping algorithm. This method could achieve the curve matching between the reference cell and target cell, and then the curve contraction coefficients can be obtained. The result shows that the method’s average error is 2.34%. Thirdly, whereas the tougher situation is that only part of the battery curve is available, we present a substitute method based on the battery degradation mechanism. This method can estimate most of the battery plant capacity through the partial battery curve. The result shows that the method’s average error is within 2%. Lastly, we contrast the applicability and limitations of every method based on the retired battery test data after deep cycling aging.

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Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives

Lin,

Xu,

Jiang

et al. 2025

Journal of Energy Chemistry

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A Deep Learning Method with Ensemble Learning for Capacity Estimation of Lithium-ion Battery

Cited by 5 publications

References 22 publications

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

Study on Capacity Estimation Methods of Second-Life Application Batteries

Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives

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