Li-ion battery prognostic and health management through an indirect hybrid model

Lyu, Zhiqiang; Wang, Geng; Gao, Renjing

doi:10.1016/j.est.2021.102990

Cited by 28 publications

(9 citation statements)

References 33 publications

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“…The experimental results show that the proposed method has better performance, and the RMSE value is reduced by 13.9% in the battery SOH estimation compared to the B-LSTM NN combined with the EMD model, 37 which is also better than the GWO-based MKRVM model 38 ; in the RUL prediction, the mean RUL error results are the same as those of the B-LSTM NN combined with EMD model, with a reduction of 5.2, 2.5, and 7.5 cycles compared to the GWO-based MKRVM, PSO-ELM-RVM, and BMA-LSTMN predictions, respectively.…”

Section: Experimental Verification and Comparative Analysismentioning

confidence: 96%

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

Zhu

Yang

Liu

et al. 2022

Journal of Algorithms & Computational Technology

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The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries describe the current aging degree of the batteries from different perspectives, and accurate and efficient battery health estimation is essential for their safe use. To improve the effectiveness and accuracy of the batteries’ health assessment models, this paper proposes a new method for SOH and RUL estimation of lithium-ion batteries. Convolutional neural networks (CNNs), bi-directional long short-term memory (BiLSTM), and attention mechanism (AM) to build a hybrid network model for capacity estimation of lithium-ion batteries, and further calculate the SOH and RUL estimation results. By using Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery capacity degradation data, we extracted the battery health indicator (HI) and verified the reasonableness of HI selection by using Gray Relational Analysis (GRA) and compared it with other network models to calculate the prediction accuracy by various evaluation indexes. The experimental results show that the method has higher estimation accuracy while avoiding the construction of complex battery mechanism degradation models and is highly generalized.

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Section: Experimental Verification and Comparative Analysismentioning

confidence: 96%

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

Zhu

Yang

Liu

et al. 2022

Journal of Algorithms & Computational Technology

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“…In [ 8 ], after considering cell formation effects, a semi-empirical model is designed to predict battery On the other hand, with the rapid development of data science and informatics methodology, data-driven model-based approach has become another popular solution for battery health management [9,10]. After collecting available battery ageing data, different data-driven models through using various machine learning (ML) technologies such as support vector machine [11], Gaussian process regression (GPR) [12], and neural network (NN) [13,14] have been successfully designed for battery health prognostics. For example, Ma et al [15] propose a hybrid NN to effectively predict the battery degradation trajectories under cyclic conditions.…”

Section: Introductionmentioning

confidence: 99%

A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems

Liu

Pan

Sun

et al. 2022

IEEE Trans. Ind. Inf.

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Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energytransportation nexus. This paper proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily-collected and time-saving accelerated ageing dataset from high temperature and SOC cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10℃, 25℃, and 45℃). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

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“…The commonly used parameter optimization algorithms include particle swarm algorithm (PSO), 31 genetic algorithm (GA), 32 Bayesian optimization algorithm (BOA), 33 and gray wolf optimization algorithm. 30 GWO is widely used for its simplicity, easy understanding, fewer parameters, and easy adjustment. Therefore, this paper improves GWO into an improved algorithm used in the MKRVM model, using GWO as the basis for the parameter search algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Realizing the optimized linear combination of multiple kernel functions is the key to build a multi‐kernel RVM. The commonly used parameter optimization algorithms include particle swarm algorithm (PSO), 31 genetic algorithm (GA), 32 Bayesian optimization algorithm(BOA), 33 and gray wolf optimization algorithm 30 . GWO is widely used for its simplicity, easy understanding, fewer parameters, and easy adjustment.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 29 pointed out that a linear combination of multiple kernel functions can combine the advantages of each kernel function and improve the accuracy of the prediction target. Lyu et al 30 used empirical modal decomposition for data pre‐processing, followed by building MKRVM for RUL prediction, achieving good forecasts on a publicly available dataset with an average RUL estimation error of only 30 cycles. Therefore, this paper uses RVM as the basis of the model and builds a new and improved MKRVM model with a combination of multiple kernel functions.…”

Section: Introductionmentioning

confidence: 99%

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Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

Qiu

Fan

Wang

et al. 2022

Intl J of Energy Research

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Summary Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models.

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Li-ion battery prognostic and health management through an indirect hybrid model

Cited by 28 publications

References 33 publications

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems

Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

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