Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method

Ma, Yan; Shan, Ce; Gao, Jinwu; Chen, Hong

doi:10.1016/j.ress.2022.108818

Cited by 38 publications

(13 citation statements)

References 39 publications

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“…In recent years, artificial intelligence (AI) technology, especially that focused on big data analysis, has made substantial advancements in numerous fields. Among such technology, deep learning methods have considerably influenced big data analysis and feature extraction, thereby garnering the interest of many researchers in the realm of HI construction [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators, typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant health indicators are beset by two principal shortcomings: (1) During traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) The performance evaluation of constructed health indicators is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint Attention LSTM-DenseNet (ALD). Firstly, artificial sample data is generated by analysing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the Attention LSTM (A-LSTM) network and DenseNet network is utilised to extract crucial feature vectors of health indicators under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system health indicators using the Euclidean distance method, and these indicators are used for predicting the system's RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the health indicators constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

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Section: Introductionmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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“…This has resulted in more accurate estimates of the remaining life of the battery, reducing maintenance costs and the risk of system failure [16]. Emerging techniques, such as transfer learning and self-supervised learning, are applied to lithium battery RUL prediction to provide better prediction performance with limited data [17]. Various deep learning models, such as convolutional neural network (CNN), and recurrent neural network [18], long short-term memory network (LSTM) [19], and transformer [20], are utilized to learn the complex relationship between battery state and RUL.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Keshun,

Guangqi,

Yingkui

2023

Meas. Sci. Technol.

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Due to the complex changes in physicochemical properties of lithium-ion batteries during the process from degradation to failure, it is difficult for methods based on physical or data-driven models to fully characterize this nonlinear process, and existing methods that hybridize physical and data-driven models suffer from ambiguous hybridization, which results in the vast majority of existing methods for predicting the RUL of lithium-ion batteries suffering from a lack of accuracy and robustness. In this study, a novel hybrid approach based on empirical modeling and data-driven techniques is proposed for predicting the remaining useful life (RUL) of lithium-ion batteries. To better capture its complexity, stochasticity, and state transition, and improve the modeling accuracy and RUL prediction precision, Gamma stochasticity and state-space modeling are used to empirically model the complex Li-ion battery degradation process. Moreover, the expectation maximization (EM) method of particle filtering (PF) was used to estimate the hidden parameters of the empirical model, and the estimated parameters were corrected using an optimized support vector regression (SVR) method to enhance the generalization performance and robustness of the data-driven model. The results show that the gamma state-space model is effective in capturing the inherent stochastic properties of the battery degradation and the proposed hybrid method outperforms the existing prediction methods in RUL prediction. The experiments show that the Sparrow Search Algorithm (SSA) optimized SVR is considered to be the most effective correction method for the estimated parameters, while the new EM-PF-SSA-SVR hybrid method provides better performance for state assessment and RUL prediction of lithium-ion batteries. It is indicated that the proposed EM-PF-SSA-SVR method with Gamma stochastic process has hybrid validity and superior performance with equal performance and less parameter computation relative to the existing state-of-the-art deep learning RUL prediction methods.

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“…The proposed method achieved an accuracy improvement of at least 5% compared to conventional LSTM methods on their laboratory dataset. Ma et al 30 . combined deep belief network and LSTM to develop SOH estimation model.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method achieved an accuracy improvement of at least 5% compared to conventional LSTM methods on their laboratory dataset. Ma et al 30 combined deep belief network and LSTM to develop SOH estimation model. Measured health indicators (HIs) such as the charging time of CC charging process, charging voltage change in the equal time interval, the time reaching to the temperature peak during discharging process, as well as calculated HIs including peak value and location of IC curves and sample entropy of discharge voltage, were used as inputs to developed SOH estimation model after applying joint distribution adaption algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian deep learning pipeline for lithium‐ion battery SOH estimation with uncertainty quantification

Ke,

Long,

Yang

et al. 2023

Quality & Reliability Eng

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In recent years, deep learning (DL) methods for state of health (SOH) estimation of lithium‐ion (Li‐ion) batteries have attracted great attention. However, most existing DL‐based methods for SOH estimation were designed using specific battery dataset. To gain better performance, the procedure can be labor‐intensive, involving designing intricate features and fine‐tuning complex DL model, which significantly limits the applications of these methods. Moreover, uncertainty quantification, that is how much uncertainty these DL‐based methods can have on their SOH estimation, cannot be addressed with only point estimation. To this end, this paper proposes a Bayesian DL pipeline for Li‐ion battery SOH estimation targeting at automatic feature extraction and uncertainty‐aware DL‐model developing for general application to batteries under different working conditions. The proposed pipeline is composed of three modules accounting for common feature extraction, automatic feature selection, and Bayesian neural network (BNN) model construction. Specifically, a frequently‐used feature set is designed using the voltage, current and time data of constant current and constant voltage (CC‐CV) charging mode, while the recursive feature elimination (RFE) with cross validation (RFECV) algorithm is used to automatically select features based on relevance. Based on the automatically selected features, DL model is automatically constructed without too much human intervention. The uncertainty of SOH estimation is quantified by BNN model using Monte Carlo (MC) dropout method, which is subsequently used to calculate the confidence of the estimation results. Four battery datasets, covering regular charging, fast charging, and second‐use applications, are used to demonstrate the performance of the proposed method. The proposed pipeline shows superior performance to state‐of‐art DL‐based methods in terms of application capability to different working conditions and uncertainty quantification for SOH estimation.

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Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method

Cited by 38 publications

References 39 publications

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

A Bayesian deep learning pipeline for lithium‐ion battery SOH estimation with uncertainty quantification

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