Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting

• URL: http://arxiv.org/abs/2202.06738v1
• Date: Mon, 14 Feb 2022 14:16:25 GMT
• Title: Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting
• Authors: Yadong Zhang, Chenye Zou and Xin Chen
• Abstract summary: The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point of view. The deep degradation network (DDN) is developed with the attention mechanism to measure similarity and predict battery capacity. The DDN model can extract the degeneration-related temporal patterns from the streaming sensor data and perform the battery capacity prediction efficiently online in real-time.
• Score: 2.8944480776764308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Battery discharge capacity forecasting is critically essential for the applications of lithium-ion batteries. The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point of view. The streaming sensor data collected by battery management systems (BMS) reflect the usable battery capacity degradation rates under various operational working conditions. The battery capacity in different cycles can be measured with the temporal patterns extracted from the streaming sensor data based on the attention mechanism. The attention-based similarity regarding the first cycle can describe the battery capacity degradation in the following cycles. The deep degradation network (DDN) is developed with the attention mechanism to measure similarity and predict battery capacity. The DDN model can extract the degeneration-related temporal patterns from the streaming sensor data and perform the battery capacity prediction efficiently online in real-time. Based on the MIT-Stanford open-access battery aging dataset, the root-mean-square error of the capacity estimation is 1.3 mAh. The mean absolute percentage error of the proposed DDN model is 0.06{\%}. The DDN model also performance well in the Oxford Battery Degradation Dataset with dynamic load profiles. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.

Related papers

• Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation [0.0]
  Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. Current methods for estimating the capacities fail to adequately account for long-term temporal dependencies of key variables. We develop a transformer-based battery capacity prediction model that accounts for both long-term and short-term patterns in battery data.
  arXiv  Detail & Related papers  (2024-07-22T20:21:40Z)
• Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves [7.07637687957493]
  This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning. Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.
  arXiv  Detail & Related papers  (2023-08-15T15:07:32Z)
• DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries [6.930255986517943]
  A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper. The DIICAN method is validated on the Oxford battery dataset.
  arXiv  Detail & Related papers  (2022-10-20T14:42:20Z)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• 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)
• Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model [0.42970700836450487]
  Battery energy storage system (BESS) can effectively mitigate the uncertainty of renewable generation. Main causes of LiB degradation are loss of Li-preventions, loss electrolyte, battery internal degradation. We propose a neural net-work based battery degradation (NNBD) model to quantify degradation with inputs of major degradation factors.
  arXiv  Detail & Related papers  (2022-02-24T23:24:52Z)
• Physics-constrained deep neural network method for estimating parameters in a redox flow battery [68.8204255655161]
  We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB) We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
  arXiv  Detail & Related papers  (2021-06-21T23:42:58Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Machine learning pipeline for battery state of health estimation [3.0238880199349834]
  We design and evaluate a machine learning pipeline for estimation of battery capacity fade. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%.
  arXiv  Detail & Related papers  (2021-02-01T13:50:56Z)
• 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.