Predicting Li-ion Battery Cycle Life with LSTM RNN

• URL: http://arxiv.org/abs/2207.03687v1
• Date: Fri, 8 Jul 2022 04:49:17 GMT
• Title: Predicting Li-ion Battery Cycle Life with LSTM RNN
• Authors: Pengcheng Xu, Yunfeng Lu
• Abstract summary: This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.
• Score: 2.4738790490814213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under different conditions. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.

Related papers

• Onboard Health Estimation using Distribution of Relaxation Times for Lithium-ion Batteries [0.0]
  We utilize impedance spectroscopy (EIS) data from 5 calendar-aged and 17 cycling-aged cells to perform state-of-health (SOH) estimation. We validate the model performance by testing it on ten different test sets, and achieve an average RMSPE of 1.69%.
  arXiv  Detail & Related papers  (2024-10-20T04:04:53Z)
• BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling [42.453194049264646]
  We introduce a machine learning-based approach for battery-type classification and address the problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2%.
  arXiv  Detail & Related papers  (2024-04-08T18:05:24Z)
• Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration [39.759100498329275]
  We present a conditional neural network framework to simultaneously achieve inter-batch calibration. We validate our approach by training and validating on different experimental batches. We extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state.
  arXiv  Detail & Related papers  (2024-03-26T12:20:10Z)
• Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data [3.739266290083215]
  We use capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering.
  arXiv  Detail & Related papers  (2023-07-17T10:42:21Z)
• Bayesian hierarchical modelling for battery lifetime early prediction [1.84926694477846]
  A hierarchical Bayesian linear model is proposed for battery life prediction. It combines both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average) The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation.
  arXiv  Detail & Related papers  (2022-11-10T17:02:39Z)
• Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level [0.8137198664755597]
  Quantile Regression Forest (QRF) model is introduced to make cycle life range prediction with uncertainty quantified. Data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. The interpretability of the final QRF model is explored with two global model-agnostic methods.
  arXiv  Detail & Related papers  (2022-04-26T16:26:27Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Data Driven Prediction of Battery Cycle Life Before Capacity Degradation [0.0]
  This paper utilizes the data and methods implemented by Kristen A. Severson, et al, to explore the methodologies that the research team used. The fundamental effort is to find out if machine learning techniques may be trained to use early life cycle data in order to accurately predict battery capacity.
  arXiv  Detail & Related papers  (2021-10-19T01:35:12Z)
• State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks [68.8204255655161]
  We build a Deep Forward Network for a lithium-ion battery and its performance assessment. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment.
  arXiv  Detail & Related papers  (2020-09-20T23:47:11Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.