Early prediction of the remaining useful life (RUL) of lithium-ion batteries remains challenging due to the weak degradation information available in early-stage data. First, a feature extractor that combines convolutional neural networks (CNN) and denoising auto-encoder based Transformers (DAE-Transformers) is proposed, which can automatically extract both local and global
Customer ServiceIn order to eliminate the influence of CRP, this paper propose a PF-AR based RUL prediction method with PF-U based CRP detection for lithium battery. Firstly, by
Customer ServiceIn order to eliminate the influence of CRP, this paper propose a PF-AR based RUL prediction method with PF-U based CRP detection for lithium battery. Firstly, by combining PF and Mann-Whitney U test theory, the battery capacity regeneration points are detected.
Customer ServiceThis paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then
Customer ServiceThe remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction
Customer ServiceAs an integral component of energy systems, the importance of Lithium-Ion (Li-ion) batteries cannot be overstated. Accurately predicting the remaining useful life (RUL) of these batteries is a paramount undertaking, as it impacts the overall reliability and sustainably of the smart manufacturing systems. Despite various existing methods have
Customer ServiceTo ensure the reliability, stability and safety of lithium-based batteries used frequently for battery energy storage systems (BESSs), such as grid-connected BESSs, accurate estimation and prediction of battery performance and health (predictive battery maintenance) in condition monitoring is necessary and very useful [4, 5].
Customer ServiceLithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault
Customer ServiceThis paper focuses on developing a Lithium-ion battery remaining practical life prediction algorithm to improve its adaptability and accuracy. To achieve this goal, the fusion
Customer ServiceThis paper focuses on developing a Lithium-ion battery remaining practical life prediction algorithm to improve its adaptability and accuracy. To achieve this goal, the fusion model methods based on data-driven, model-driven and the combination of the two are summarized, and the problems they face are discussed. Accurate estimation of the
Customer ServiceAccurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble
Customer ServiceIn order to address the above problems, this paper proposes an accurate, efficient, and interpretable battery remaining life prediction method that optimizes the prediction process from both the data source and model structure to reduce computation source consumption and speed up prediction while ensuring accurate prediction, the detailed
Customer ServiceAfter the CRP detection is completed, the PF and ARIMA models are then employed to predict the RUL of the lithium-ion battery. Download: Download high-res image (129KB) Download : Download full-size image; Fig. 1. Impact of CRP on the accuracy of RUL prediction. 2.2. CRP detection based on PF-W-distance. The PF algorithm derives from Monte
Customer ServiceIn order to address the above problems, this paper proposes an accurate, efficient, and interpretable battery remaining life prediction method that optimizes the prediction process from both the data source and model structure to reduce computation source consumption and
Customer ServiceThe remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach
Customer ServiceAccurate prediction of the remaining useful life (RUL) is a key function for ensuring the safety and stability of lithium-ion batteries. To solve the capacity regeneration and model adaptability under different working
Customer ServiceIn response to the current issue of low accuracy and robustness in the remaining useful life (RUL) model of lithium-ion batteries. In the framework of AdaBoost, a lithium-ion battery life prediction model based on an improved whale optimization algorithm to optimize the Kernel Extreme Learning Machine (IWOA-KELM) is proposed. The IWOA-KELM model is used as a
Customer ServiceRemaining useful life (RUL) prediction of lithium-ion battery is critical for the normal operation of electric vehicles. In conventional approaches, the adaptive estimation of
Customer ServiceAccurate prediction of the remaining useful life (RUL) is a key function for ensuring the safety and stability of lithium-ion batteries. To solve the capacity regeneration and model adaptability under different working conditions, a hybrid RUL prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN
Customer ServiceAbnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection
Customer ServiceThe state of health (SOH) of lithium-ion (Li+) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li+ batteries. By
Customer ServiceAdditionally, predicting the remaining service life of lithium-ion batteries poses challenges due to diverse aging processes, extensive battery variability, and dynamic operating conditions . It is crucial to accurately estimate and forecast the battery''s state to enhance its reliability and ensure the safety and stability of the system [ 6 ].
Customer ServiceThis paper focuses on developing a Lithium-ion battery remaining practical life prediction algorithm to improve its adaptability and accuracy. To achieve this goal, the fusion model methods based on data-driven, model-driven and the combination of the two are summarized, and the problems they face are discussed. Accurate estimation of the remaining
Customer ServiceThe remaining useful life (RUL) prediction of lithium-ion battery is essential for health management, which can guide the time of battery replacement. However, environmental factors, measurement Expand
Customer ServiceAs an integral component of energy systems, the importance of Lithium-Ion (Li-ion) batteries cannot be overstated. Accurately predicting the remaining useful life (RUL) of
Customer ServiceThe remaining useful life (RUL) prediction of lithium-ion battery is essential for health management, which can guide the time of battery replacement. However,
Customer ServiceRemaining useful life (RUL) prediction of lithium-ion battery is critical for the normal operation of electric vehicles. In conventional approaches, the adaptive estimation of model parameters and the detection of capacity regeneration await further research.
Customer ServiceRen L, Dong JB, Wang XK, et al. A data-driven auto-CNN-LSTM prediction model for lithium-ion battery remaining useful life. IEEE Trans Ind Inf 2021; 17(5): 3478–3487. Crossref. Google Scholar. 7. Wang SL, Takyi-aninakwa P, Jin SY, et al. An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of
Customer ServiceTo ensure the reliability, stability and safety of lithium-based batteries used frequently for battery energy storage systems (BESSs), such as grid-connected BESSs,
Customer ServiceAccurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems.
Customer ServiceFirstly, the CRP detection is carried out based on PF and Mann Whitney U test. Then, a hybrid model combining PF and AR model is proposed to predict the RUL of lithium batteries. The predicted value of AR model is taken as the actual value to update the parameters of the PF model to achieve accurate RUL prediction.
Because the existence of the self-recovery phenomena will influence the battery's average deterioration trend, the lithium-ion battery's RUL prediction will indeed affect the prediction accuracy. In addition, this self-recovery capacity phenomenon must exist during each battery's everyday use.
Dalal et al. established a particle filtering framework for estimating the life of lithium-ion batteries, which makes use of a lumped parameter battery model to describe all of the battery's dynamic features. Kozlowski built a two-electrode electrochemical model of the battery and verified it using measured impedance data.
Ren et al. proposed ADNN, an integrated deep-learning method for forecasting the life of lithium batteries that combines autoencoder and DNN. This method is used to estimate how long several lithium-ion batteries will last.
Therefore, it is still challenging to predict the RUL of lithium-ion batteries considering the self-recovery effect of capacity. The large-scale application of lithium-ion batteries in various fields puts forward high requirements for their reliability and safety, making the remaining life prediction of lithium-ion batteries a research hotspot.
To ensure the lithium-ion battery system's reliable operation, a process must be in place to assess the lithium-ion battery system's State of Health (SOH) and estimate the RUL, which can assist manufacturers in determining when to remove or replace lithium-ion battery reference information.
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