Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

Khaleghi, Sahar; Hosen, Sazzad; Karimi, Danial; Behi, Hamidreza; Beheshti, S. Hamidreza; Mierlo, Joeri Van; Berecibar, Maitane

doi:10.1016/j.apenergy.2021.118348

Cited by 107 publications

(25 citation statements)

References 59 publications

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“…An enhanced ELM model was constructed by nonparametric aging analysis to realize the storage and cyclic operation of the experimental data. The improved Gaussian regression and nonlinear regression were used to construct a multi-timescale framework to predict battery SOH [66]. A collaborative Gaussian processing regression model for capacity trend transfer learning between batteries was established.…”

Section: Mathematical Modeling Of Deep Learningmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

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Section: Mathematical Modeling Of Deep Learningmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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“…For example, an accurate electro-thermal model is required for electrical and thermal issues in the developed management system [ 35 ]. Moreover, an accurate tool is needed for state of charge (SoC) [ 36 ], state of health (SoH) [ 37 ], and state of power (SoP) to control the LiC system [ 38 ]. The holistic model of the LiC that is applicable for real-time energy management and control purposes is shown in Figure 7 [ 26 ].…”

Section: 1d Electrical Thermal and Lifetime Modelingmentioning

confidence: 99%

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

et al. 2022

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This review paper aims to provide the background and literature review of a hybrid energy storage system (ESS) called a lithium-ion capacitor (LiC). Since the LiC structure is formed based on the anode of lithium-ion batteries (LiB) and cathode of electric double-layer capacitors (EDLCs), a short overview of LiBs and EDLCs is presented following the motivation of hybrid ESSs. Then, the used materials in LiC technology are elaborated. Later, a discussion regarding the current knowledge and recent development related to electro-thermal and lifetime modeling for the LiCs is given. As the performance and lifetime of LiCs highly depends on the operating temperature, heat transfer modeling and heat generation mechanisms of the LiC technology have been introduced, and the published papers considering the thermal management of LiCs have been listed and discussed. In the last section, the applications of LiCs have been elaborated.

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“…Currently, the methods of RUL prediction have been divided into three main categories: the model-based methods, the data-driven methods, and the hybrid model methods. [9][10][11][12] The model-based methods are introduced to analyze the capacity fading of lithium-ion batteries by establishing an equivalent model. Therefore, it is very significant to establish an accurate battery model, including the electrochemical model, the empirical degradation model, and the equivalent circuit model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Currently, the methods of RUL prediction have been divided into three main categories: the model‐based methods, the data‐driven methods, and the hybrid model methods 9‐12 . The model‐based methods are introduced to analyze the capacity fading of lithium‐ion batteries by establishing an equivalent model.…”

Section: Introductionmentioning

confidence: 99%

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

Wei

Wang

et al. 2022

Intl J of Energy Research

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Summary To accurately predict the remaining useful life (RUL) of lithium‐ion battery, this paper presents a novel machine‐learning method using gated recurrent unit (GRU) with error compensation (EC). Moreover, the dropout Monte Carlo technology is selected for reliable RUL prediction with uncertainty quantification. First, the equal charging voltage time is established and the variational mode decomposition is introduced to reduce the influence of capacity regeneration phenomenon. Then, the phase space reconstruction with C‐C technology is adopted to obtain the optimal input sequence, and the GRU with EC is established for RUL prediction. Finally, the probability distribution and 95% confidence interval are obtained based on dropout Monte Carlo technology. Compared with the existing methods, the proposed method can not only obtain a higher accuracy with a mean absolute error below 1.55% but also achieve reliable RUL prediction with probability distribution.

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Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

Cited by 107 publications

References 59 publications

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

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