Deep neural network battery life and voltage prediction by using data of one cycle only

Hsu, Chia Wei; Xiong, Rui; Chen, Nan Yow; Li, Ju; Tsou, Nien‐Ti

doi:10.1016/j.apenergy.2021.118134

Cited by 74 publications

(25 citation statements)

References 26 publications

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“…Hsu et al 30 also used a CNN to conduct early battery cycle life prediction by using data collected from the initial few cycles as input. These studies [28][29][30] avoid feature engineering and show higher accuracy than conventional studies based on handcrafted features. 21 Despite the advances in life prediction of batteries, some crucial challenges remain unresolved.…”

Section: Introductionmentioning

confidence: 99%

“…Hong et al 29 developed a convolutional neural network (CNN) that takes measured current, voltage and temperature as input to predict battery life. Hsu et al 30 also used a CNN to conduct early battery cycle life prediction by using data collected from the initial few cycles as input. These studies 28–30 avoid feature engineering and show higher accuracy than conventional studies based on handcrafted features 21 …”

Section: Introductionmentioning

confidence: 99%

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Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Tian

Xiong

Shen

et al. 2022

EcoMat

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With the wide deployment of rechargeable batteries, battery degradation prediction has emerged as a challenging issue. However, battery life defined by capacity loss provides limited information regarding battery degradation. In this article, we explore the prediction of voltage‐capacity curves over battery lifetime based on a sequence to sequence (seq2seq) model. We demonstrate that the data of one present voltage‐capacity curve can be used as the input of the seq2seq model to accurately predict the voltage‐capacity curves at 100, 200, and 300 cycles ahead. This offers an opportunity to update battery management strategies in response to the predicted consequences. Besides, the model avoids feature engineering and is flexible to incorporate different numbers of input and output cycles. Therefore, it can be easily transplanted to other battery systems or electrochemical components. Furthermore, the model features data generation, that is, we can use the data of only one cycle to generate a large spectrum of aging data at the future cycles for developing other battery diagnosis or prognosis methods. In this way, the time and energy consuming battery degradation tests can be sharply reduced.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Tian

Xiong

Shen

et al. 2022

EcoMat

Self Cite

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“…Lithium-ion batteries have become an ideal power source for electric vehicles because of their high energy density, long cycle life, and low self-discharge rate [2]. The batteries will age with repeated use, which will decrease the battery's charge and discharge capacity and the actual remaining capacity [3,4]. State of health (SOH) is a quantitative indicator used for evaluating the degree of battery aging.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine Using Bat Optimization Algorithm for Estimating State of Health of Lithium-Ion Batteries

et al. 2022

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An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and good generalization performance. The bat algorithm (BA) is a swarm intelligence optimization algorithm based on bat echolocation for foraging. In this study, BA was creatively applied to improve the ELM neural network, forming a BA-ELM model, and it was applied to SOH estimation for the first time. First, through Pearson and Spearman correlation analysis, six variables were determined as the input variables of the model. The actual remaining capacity of the battery was determined as the output variable. Second, BA was used to optimize the connection weights and bias in ELM to construct the BA-ELM model. Third, the battery data set was trained and tested with BA-ELM, ELM, Elman, back propagation (BP), radial basis function (RBF), and general regression neural network (GRNN) models. Five statistical error indicators, and the radar chart, scatter plot, and violin diagram were used to compare the estimation effects. The results show that the evaluation function of BA-ELM can converge quickly and effectively optimize the network model of ELM. The RMSE of the BA-ELM model was 0.5354%, and the MAE was 0.4326%, which is the smallest among the 6 models. The RMSE values of the other model were 2.27%, 3.53%, 3.07%, 3.86%, 3.24%, respectively, indicating the BA-ELM has good potential for future applications.

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“…33 Among them, the end-toend data-driven method directly uses the raw data as input, avoiding the complex handcrafted feature extraction process. Recently, data-driven methods have successfully achieved reliable degradation prediction, 34 voltage prediction, 35 and open-circuit voltage reconstruction 36 of LIBs using data collected directly from the cycle. However, there are few studies using data-driven methods to detect lithium plating.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

Tian

Lin

et al. 2022

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Lithium plating seriously threatens the life of lithium-ion batteries at low temperatures charging conditions, but the onboard detection and quantification of lithium plating are severely hampered by the limited available signals and volatile operating conditions in real scenarios. Herein, we propose a detection method to predict the occurrence and quantification of lithium plating under uncertain conditions by only using constant-current curves during charging based on deep learning. A deep neural network (DNN) is developed to extract data-driven features induced by lithium plating from the charge curves, avoiding the challenge of manual feature selection. Only using the most common voltage and current signals as inputs, the network exhibits superior adaptability and accuracy. The detection accuracy of the proposed method is 98.64%, while the quantity of the lithium plating can be accurately predicted with a root-mean-square error <4.1712 mg. Moreover, the generalization ability of the proposed method is verified by its reliable detection accuracy under conditions that are not used in the training dataset. The detection accuracy is 92.39% for brand new charging conditions and 95.53% for brand new aging states. This method shortens the detection time that currently takes more than several hours (the widely used differential curve analysis) to milliseconds and eliminates the need for a rigorous testing environment, showing great potential for onboard application in future battery management systems.

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Deep neural network battery life and voltage prediction by using data of one cycle only

Cited by 74 publications

References 26 publications

Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Extreme Learning Machine Using Bat Optimization Algorithm for Estimating State of Health of Lithium-Ion Batteries

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

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