Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

Zhang, Shuzhi; Zhai, Bin; Xu, Guo; Wang, Kaike; Peng, Nian; Zhang, Xiongwen

doi:10.1016/j.est.2019.100951

Cited by 205 publications

(69 citation statements)

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“…Markiewicz et al [42] finds LSTM-RNN to benefit from being relative stable, notably because it does not suffer from the vanishing or exploding gradient problem [73,74]. Zhang et al [33] states that LSTM-RNN has good performance on sequential data due to the recurrent feedback. Finally, as argued by Nguyen and Medjaher [40], LSTM-RNN benefits from having a long-term memory meaning it can keep important information for later application.…”

Section: Annmentioning

confidence: 99%

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Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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Section: Annmentioning

confidence: 99%

“…Some components can have a steady linear degradation at the start of their lifetime, but then suddenly start dropping by the end of it. This is common within e.g., batteries [30,33,54]. Hence, an issue of determining a hidden state process.…”

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…11 Based on advanced machine learning algorithms and without sophisticated battery model, databased methods take selected battery external parameters as model inputs, which include current, voltage and ambient temperature, to realize online battery SOC estimation. 12,13 The machine learning algorithms include Gaussian process regression (GPR), [14][15][16] support vector machine (SVM), [17][18][19] neural network (NN), [20][21][22] fuzzy logic (FL), 23,24 and so on. Sahinoglu et al 14 proposed an original SOC estimation method using recurrent/regular GPR framework, where both simulation and experimental results verified the high estimation accuracy of this method.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, the last two types of methods have attracted tremendous attention due to their own advantages 11 . Based on advanced machine learning algorithms and without sophisticated battery model, data‐based methods take selected battery external parameters as model inputs, which include current, voltage and ambient temperature, to realize online battery SOC estimation 12,13 . The machine learning algorithms include Gaussian process regression (GPR), 14‐16 support vector machine (SVM), 17‐19 neural network (NN), 20‐22 fuzzy logic (FL), 23,24 and so on.…”

Section: Introductionmentioning

confidence: 99%

Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

Peng

Zhang

et al. 2020

Int J Energy Res

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State of charge (SOC) is a vital parameter which helps make full use of battery capacity and improve battery safety control. In this paper, an improved adaptive dual unscented Kalman filter (ADUKF) algorithm is adopted to realize coestimation of the battery model parameters and SOC. Notably, the covariance matching method that can adapt the system noise covariance and the measurement noise covariance is used to improve the estimation accuracy. Besides, singular value decomposition (SVD) is utilized to deal with the non-positive error covariance matrix in both unscented Kalman filters, further enhancing the stability of estimation algorithm. Verification results under Dynamic Stress test and Federal Urban Driving Schedule test indicate that improved ADUKF can achieve more accurate SOC estimates with error band controlled within 2.8%, while that of traditional dual unscented Kalman filter (DUKF) can only be controlled within 5%. Moreover, robustness analysis is also conducted and the validation results present that the proposed algorithm can still provide precise SOC prediction results under some disturbances, such as erroneous initial SOC, inaccurate battery capacity, and various ambient temperatures.

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“…These methods are model-free, and do not need prior knowledge on the complex working principles of the battery. Various ML techniques have been applied to estimate the battery capacity fade, such as neural networks (NNs) (Dai et al, 2018;You et al, 2016;Zhang et al, 2019), recurrent neural network (RNN) (Chaoui and Ibe-Ekeocha, 2017;Eddahech et al, 2012), support vector machine (SVM) (Liu et al, 2018), support vector regression (SVR) (Weng et al, 2013), and relevance vector machine (RVM) (Guo et al, 2019;Hu et al, 2015), just to name a few. In You et al (2016), a NN with various optimization strategies is used for capacity estimation, by combining with the k-means clustering algorithm, achieving a RMSE of less than 2.44%.…”

Section: Introductionmentioning

confidence: 99%

Fast battery capacity estimation using convolutional neural networks

et al. 2020

Transactions of the Institute of Measurement and Control

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Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

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Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

Cited by 205 publications

References 50 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

Fast battery capacity estimation using convolutional neural networks

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