Early prediction of battery lifetime via a machine learning based framework

Fei, Zicheng; Yang, Fangfang; Tsui, Kwok‐Leung; Li, Lishuai; Zhang, Zijun

doi:10.1016/j.energy.2021.120205

Cited by 157 publications

(56 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To shorten the model training time, the number of health feature selection can be reduced during the experiment. In addition, some dimensionality reduction methods, such as principal component analysis (PCA) ( Fei et al., 2021 ) and local linear embedding (LLE) algorithm ( Hong and Zeng, 2021 ), may also be an effective manner to reduce the dimension of health features and correspondingly shorten the model training time.…”

Section: Challenges and Future Trend Of Machine Learning Methodsmentioning

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

Section: Challenges and Future Trend Of Machine Learning Methodsmentioning

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

show abstract

“…[102][103][104] Generally, ML uses a fitting function from the experimental training data to make predictions for other battery systems. Various models such as linear model, [54,105] ANNs, [106][107][108][109][110][111][112][113] SVM, [114][115][116][117][118][119][120] RF, [121][122][123] Kalman filters, [124][125][126] gated recurrent unit recurrent neural network, [127] convolutional neural network, [128,129] DNN, [130] JAYA, [131] metabolic extreme learning machine, [132] and Gaussian/Bayesian regression, [133][134][135] have been reported to be able to predict the states of batteries. Below we will elaborate some of the most recent DMMs employed to estimate the different battery states for LIBs.…”

Section: Battery State Predictionmentioning

confidence: 99%

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

et al. 2021

View full text Add to dashboard Cite

performance and cost are not satisfactory while the energy density of conventional LIBs have almost reached the theoretical maximum. To further advance current LIBs, numerous efforts have been devoted to the exploration of new electrode and electrolyte materials. [3] The historical material research has heavily relied on either "trial-and-error" processes or serendipity, both of which require the vast numbers of tedious experiments (Figure 1a). Such intuition-based approaches are often time-consuming and inefficient, which cannot avoid the consumption of many manpower and material resources. In the past 50 years, computational chemistry, such as firstprinciples (FP) calculations, [4,5] quantum mechanics, [6] molecular dynamics (MD) [7] and Monte Carlo techniques, [8] has become a mature approach to complement and aid experimental studies for predicting and designing new materials. With the rapid development of high-performance computations, density functional theory (DFT) has been widely applied to high-throughput property prediction, which is conducive to the development of materials databases, such as Inorganic Crystal Structure Database (ICSD), [9] Cambridge Structural Database, [10] the Materials Project [11] database, AFLOWLIB consortium, [12] Open Quantum Materials Database, [13] Harvard Clean Energy Project, [14] Electronic Structure Project, [15] MaterialGo, [16] and so on. However, Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

show abstract

“…The indicators (interval points and integral of voltage vs. time as features; a time constant from the charging current curve; minimum and average temperature; IC-based and DV-based; cycle number etc.) from the charging curves can be explored because the charging curves are stable and the charging profile is usually fixed before charging is carried out [33][34][35][36][37][38]. The selected indicators from the charging curves shall be mapped to estimate the SoC, SoH and RUL using deep learning networks combined with transfer learning.…”

Section: Research Problemmentioning

confidence: 99%

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Garg

Mou

et al. 2022

IET Collab Intel Manufact

View full text Add to dashboard Cite

Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series‐parallel) during its operation in the pack for meeting the non‐linear time‐dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low‐accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi‐objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real‐time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.

show abstract

Early prediction of battery lifetime via a machine learning based framework

Cited by 157 publications

References 45 publications

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

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

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Contact Info

Product

Resources

About