Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

Chen, Zheng; Xue, Qiao; Wu, Yitao; Shen, Shiquan; Zhang, Hongjie; Shen, Jiangwei

doi:10.1109/access.2020.3025766

Cited by 21 publications

(6 citation statements)

References 34 publications

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“…11 Indeed, these deep learning methods have been previously applied to this dataset. 8,[26][27][28][29][30] However, neural networks typically require a large number of samples, are computationally expensive, require deep learning domain expertise, andmost importantly for our purposes-are often difficult to interpret (both physically and statistically). Our interest in this work is achieving high accuracy while maintaining interpretability.…”

Section: Summary Of Dataset and Previous Workmentioning

confidence: 99%

“…Since publication of the Severson et al 3 dataset, others have applied advanced machine learning and deep learning methods, including relevance vector machines, 5,8 gradient boosted regression trees, 25 Gaussian process regression, 8 recurrent neural networks (including long short-term memory networks), 8,26 and convolutional neural networks. 8,[27][28][29][30] Many of these works have explored creative approaches, including data augmentation 27 and the use of differential capacity analysis.…”

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confidence: 99%

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Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Attia

Severson²,

Witmer³

2021

J. Electrochem. Soc.

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Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the “capacity matrix” concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset; thus, our work can serve as a comprehensive benchmarking study for this dataset. These models also provide insights into the degradation of these cells. Our approaches can be used both to quickly train models for a new battery cycling dataset and to benchmark the performance of more advanced machine learning methods.

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Section: Summary Of Dataset and Previous Workmentioning

confidence: 99%

mentioning

confidence: 99%

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Attia

Severson²,

Witmer³

2021

J. Electrochem. Soc.

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“…Each gradient calculation needs to conduct the inversion operation of the covariance matrix. When dealing with large dataset, the amount of calculation will become a bottleneck to restrict the application of GPR ( Chen et al., 2020 ). In practical applications, the training time of constructing an ML algorithm with acceptable performance may take up to tens of minutes.…”

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

Shu

Shen

et al. 2021

iScience

Self Cite

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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.

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“…In order to overcome the long-term dependence of RNN, Jürgen Schmidhuber et al proposed a recursive neural network with long and short-term memory in 1997 34 . Compared with simple RNN, LSTM adds a state in the hidden layer to maintain the long-term state, and this newly added state is called cell state 35 . The input of LSTM includes the output value, the input value and the cell state of the previous time, and the output includes the output value and the cell state of the current time.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

Dong

et al. 2021

Sci Rep

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The energy storage system is an important part of the energy system. Lithium-ion batteries have been widely used in energy storage systems because of their high energy density and long life. However, the temperature is still the key factor hindering the further development of lithium-ion battery energy storage systems. Both low temperature and high temperature will reduce the life and safety of lithium-ion batteries. In actual operation, the core temperature and the surface temperature of the lithium-ion battery energy storage system may have a large temperature difference. However, only the surface temperature of the lithium-ion battery energy storage system can be easily measured. The estimation method of the core temperature, which can better reflect the operation condition of the lithium-ion battery energy storage system, has not been commercialized. To secure the thermal safety of the energy storage system, a multi-step ahead thermal warning network for the energy storage system based on the core temperature detection is developed in this paper. The thermal warning network utilizes the measurement difference and an integrated long and short-term memory network to process the input time series. This thermal early warning network takes the core temperature of the energy storage system as the judgment criterion of early warning and can provide a warning signal in multi-step in advance. This detection network can use real-time measurement to predict whether the core temperature of the lithium-ion battery energy storage system will reach a critical value in the following time window. And the output of the established warning network model directly determines whether or not an early emergency signal should be sent out. In the end, the accuracy and effectiveness of the model are verified by numerous testing.

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Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

Cited by 21 publications

References 34 publications

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

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

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

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