A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current

Xu, Tingting; Peng, Zhen; Wu, Lifeng

doi:10.1016/j.energy.2020.119530

Cited by 60 publications

(28 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel predicting method for circulating capacity of LIB is suggested considering the effect of random variable current (RVC) on battery capacity degradation. The minimum battery capacity RMSE predicted is 0.010294 and cycle capacity error range is -3mAh to 3mAh [93].…”

Section: A Battery Thermal Management (Btm)mentioning

confidence: 93%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

View full text Add to dashboard Cite

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

show abstract

Section: A Battery Thermal Management (Btm)mentioning

confidence: 93%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To account for the impact of random time-varying discharge current, Xu et al. develops an OSELM based on the beetle antenna search (BAS) algorithm ( Xu et al., 2021a ). To improve the online learning ability and update mechanism of OSELM, Tian et al.…”

Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

View full text Add to dashboard Cite

Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

show abstract

“…The rule of lithium ion battery performance degradation is directly mined from the data of lithium ion battery voltage, current, temperature, and capacity, and the nonlinear quantitative model of degradation rule or battery health state is automatically established, which has strong applicability. Common datadriven methods include support vector machine (SVM) , particle filter (PF) (Lyu et al, 2021b), deep learning network (Kaur et al, 2021;Sun et al, 2021), extreme learning machine (Xu et al, 2021), K-nearest neighbor regression (Zhou et al, 2020), etc. A battery capacity estimation method is proposed based on dynamic time warping algorithm in the study by Liu et al (2019), which can quickly estimate the capacity of each battery in the battery pack by using the previous charging curve and current charging data of one battery in the battery pack.…”

Section: Introductionmentioning

confidence: 99%

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

2022

View full text Add to dashboard Cite

Battery health prediction is very important for the safety of lithium batteries. Due to the factors such as capacity regeneration and random fluctuation in the use of lithium ion battery, the accuracy and generalization ability are poor when using a single scale feature to predict the health state of lithium ion battery. To solve these problems, we propose a comprehensive prediction method based on variational mode decomposition, integrated particle filter, and long short-term memory network with self-attention mechanism. Firstly, the capacity data of lithium ion battery is decomposed by variational mode decomposition to obtain the residual component which can reflect the global degradation trend of lithium ion battery and intrinsic mode functions component that can reflect the local random fluctuation. Then, the particle filter algorithm is employed to predict the residual component, and the long short-term memory network with self-attention mechanism is proposed to predict the intrinsic mode functions component. Finally, the prediction results of each subcomponent are reconstructed to obtain the final prediction value of lithium ion battery health state. The experimental results show that the prediction method proposed in this article has good prediction accuracy and stability.

show abstract

A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current

Cited by 60 publications

References 32 publications

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

Contact Info

Product

Resources

About